MVSMamba: Multi-View Stereo with State Space Model
Authors: Jianfei Jiang
Qiankun Liu
Hongyuan Liu
Haochen Yu
Liyong Wang
Jiansheng Chen
Huimin Ma
Venue: NeurIPS 2025 poster ()
Keywords: Multi-View Stereo
Depth Estimation
Mamba
Primary area: applications

Abstract: Robust feature representations are essential for learning-based Multi-View Stereo (MVS), which relies on accurate feature matching. Recent MVS methods leverage Transformers to capture long-range dependencies based on local features extracted by conventional feature pyramid networks. However, the quadratic complexity of Transformer-based MVS methods poses challenges to balance performance and efficiency. Motivated by the global modeling capability and linear complexity of the Mamba architecture, we propose MVSMamba, the first Mamba-based MVS network. MVSMamba enables efficient global feature aggregation with minimal computational overhead. To fully exploit Mamba's potential in MVS, we propose a Dynamic Mamba module (DM-module) based on a novel reference-centered dynamic scanning strategy, which enables: (1)  Efficient intra- and inter-view feature interaction from the reference to source views, (2) Omnidirectional multi-view feature representations, and (3) Multi-scale global feature aggregation. Extensive experimental results demonstrate MVSMamba outperforms state-of-the-art MVS methods on the DTU dataset and the Tanks-and-Temples benchmark with both superior performance and efficiency. The source code is available at https://github.com/JianfeiJ/MVSMamba.

MVSMamba: Multi-Vies View Stereo with State Space Model
Jianfei Jiang
Qiankun Liu" Hongyuan Liu Haochen Yu
Liyong Wang Jiansheng Chen Huimin Ma*
University of Science and Technology Beijing, China
{jiangjf hoogyuannii haochen yu, wangly)0xs wwt.uuutbb.... edu. {liugk3, jschen, wwwmmb)mmpbam...mm Sesttuubuuuuu.uuuuu edu.
Abstract
Robust feature representations are essential for Cearning-basek Multi-View Stereo (MVS), which relies on accurate feature matching. Recent MVS methods leverage Transformers to capture long-range dependencies based on local features extracted by conventional feature pyramid networks. However, the quadratic complexity Transformer-base MVS methods poses challenges to balance performance and efficiency. Motivated by the global modeling capability and linear complexity of the Mamba architecture, propose MVSMamba, the first Mamba-based MVS network. MVSMamba enables efficient global feature aggregation with minimal computational overhead. To fully exploit Mamba's potential in MVS, we propose a Dynamic Mamba module (DM-module) based on a novel reference- centered dynamic scanning strategy, which enables: (1) Efficient intra- and inter- view feature interaction from the reference to source views, (2) Omnidirectional multi-view feature representations, and (3) Multi-scale global feature aggregation. Extensive experimental results demonstrate MVSMamba outperforms state-of-the art MVS methods on the DTU dataset and the manks-and-Templee benchmark with both superior performance and efficiency. The source code is available at http://gghiubccoo//aaaaii////aaa
Introduction
Multi-View Stereo (MVS) is aims to reconstruct dense 3D geometry of objects scenes from calibrated multi-view images, which is widely used in fields like autonomous driving [12) It estimates the depth of each pixel by identifying correspondences across source views that satisfy multi-view geometric consistency, making the task highly dependent on the quality of feature matching. Naturally, more robust feature representations lead more reliable feature matching.
Traditional MVS methods 3
S rely on handcrafted features, which tend to perform poorly in regions with repetitive patterns, weak textures, and reflections. In contrast, learning- based MVS have made significant strides by leveraging the strong representational power of deep neural networks. Early learning based MVS methods utilize Convolutional Neural Networks [6] for feature extraction, but their limited receptive fields methods have introduced Transformers [17 to
methods 9 10
(CNNs) 12 and their variants [3] [5]
restricted to local features only. Recently, model long-range dependencies, enabling global feature learning both within and across views, thus improving the robustness of feature matching in challenging regions.
Despite significant progress, Transformer- based MVS methods [18 19 20 continue to suffer from quadratic computational complexity. To mitigate this issue, existing approaches have introduce linear
*Corresponding authors
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

0.32
68
CosMV/NNtt
UiMVVSNNt
Intermeidate F-score (*o)
43
Overnil Error (nun)
0.31
DMVSNet
42.
GaMVS
66-
41
0.30
EI-MVSKO SNat
MVSFoommer
M/SRomee++
65
0.29
(Ohmg)
64
38
0.28
37-
2
6
8
10
12
0.1
0.2
0.3
04
0.5
0.6
0.7
10
15
35 40
(a) Memory (G)
Runtime
(e) (M)
Figure Comparison performance vs. efficiency among state-of-the-art CNN-based and our Mamba-based (*) methods on the (a) DTU dataset, the Tanks-and- Temples (b) intermediate and (c) advanced benchmark. The GPU memory and runtime are evaluated on 5-view images with resolution of 832x 1152. The proposed MVSMamba achieves the best performance with superior efficiency.
Transformer-based
attention 18 21 epipolar window attention 22 23 and epipolar vanilla attention 17 and typically applying them at the lowest resolution stage to reduce computational cost. However, these methods still involve multiple rounds of self-attention and cross-attention across both reference and source features, resulting in substantial overhead. Consequently, striking an optimal balance between performance and efficiency remains major challenge in MVS. A critical question remains: How can we sustain high performance while minimizing computational cost?
As powerful variant of state space models [25], Mamba [26] offers promising solution by enabling effective modeling
dependencies with linear complexity. Inspired by this, we propose MVSMamba, the first MVS network to explore the Mamba architecture. MVSMamba is designed to efficiently model long-range dependencies among multi-view features, addressing performance and efficiency bottlenecks in challenging MVS scenarios. To incorporate Mamba into the multi-view feature matching process, we introduce novel Dynamic Mamba module (DM-module) based on reference-centeren dynamic scanning strategy. This module facilitates the efficient learning of global and omnidirectional feature representations across multiple views. Specifically, for each reference-source feature pair, the source features are concatenated to the top, bottom, left, and right of the reference features, enabling four directional scanning from the reference view toward the source view. This spatial configuration enhances intra- and inter-view interactions for each feature pair. To generalize beyond pairwise matching, we dynamically adjust the scan directions based on the index of source views, performing omnidirectional scanning from the reference feature toward different source views. This enables the construction of omnidirectional multi-view feature representations. The DM-module is deployed multiple scales within the FPN capture long-range dependencies
across different spatial resolutions. As shown in Fig. [ CNN-based methods [1] 27 28 29] are
relatively efficient but suffer from limited performance. Transformer-basee offer superior performance, but this often comes at the cost of reduced efficiency. In contrast, the proposed MVSMamba achieves state-of-the-arr performance both the DTU dataset and the Tanks-and Temples
methods [18 24 30 20
-nd--eplesss benchmark, while offering superior efficiency.
The main contributions are summarized as follows:
We present MVSMamba, the first MVS network to leverage the Mamba architecture, en- abling efficient global and omnidirectional multi-view feature representation.
We propose novel Dynamic Mamba module based on a reference-centered dynamic scan- ning strategy effectively bridge directional scanning with multi-view feature aggregation.
Extensive experiments the DTU dataset and the Tanks-and-Temple benchmark demon- strate that MVSMamba achieves state-of-the-art performance with superior efficiency.
Related Work
2.1 Learning-based MVS
In recent years, learning-based MVS mehthods 9 31 have made significant progress compare with traditional methods. MVSNet [9] presents the first end-to-end Marning-basee MVS method,

---

leveraging Convolutional Neural Network (CNN) for feature extraction. Subsequent methods have introduced improvements from various perspectives. RNN-based MVS methods [TC 22 reduces memory consumption but suffers from slow inference speed. Iterative update-based MVS meth- 37 enabling high efficiency but with limited performance. Coarse-to- fine MVS
ods 33
34
35
36
methods 11
38
39 achieves relatively better trade-off between performance and efficiency, and
have gradually become the dominant paradigm.
Coarse-to-fine MVS methods typically employ Feature Pyramid Networks 12 (FPN) to extract multi-scale features, enabling depth estimation at multiple resolutions. Some later works [4 [8]
adopt CNN variants [15] to enhance feature representations. However, due to the limited receptive field of CNNs, these methods are constrained to capture local features. To address this. TransMWVS Net [8] first introduces Transformers [77 to MVS, employing intra- and inter- view attention [2]
to aggregate global features. WT-MVSNet [2 incorporates Swin Transformer [23] and constraines feature aggregation within epipolar-alignes windows. ET-MVSNet (24) further restrictes vanilla atten- tion [17 to epipolar line pairs, enabling non-local feature aggregation. Moreover, MVSFormer [30], MVSFormer++ + (20 and MonoMVSNet [4]] enhance FPN features using pretrained Vision Trans- (VIT). Although these Transformer-basee MVS methods have made efforts to address the complexity issues inherent in attention mechanisms, they either inevitably require alternating computations of self-attention and cross- attention to construct long-range dependency, or rely parameter-heaa heavy pretrained models, making it difficult to simultancously achieve high performance and efficiency.
formers 41
2.2 State Space Models
Transformers [17] have substantially advanced in computer vision but are hindered by their quadratic complexity. To mitigate this limitation, researchers have developed more efficient alternatives, including linear attention
[2I], shifted window attention [23], and flash attention [44). Concurrently,
state space models [25). combined with selective mechanisms, have gained traction for capturing long-range dependencies with linear complexity (detailed in Appendix (A). Recently, Mamba (66 has
shown promising performance in computer vision tasks [45] 46 47). Vim [48] and VMamba 44
expand receptive fields globally using bidirectional and Four-directional scanning, respectively.
EVMamba (50) introduces a skip-scan mechanism to improve computational efficiency. Subsequent works, including Mamba Vision [5T], EfficientViM [52]. and Mamba-ND(33,, further explored this domain by combining Mamba with self-attention, reducing computational costs, or extending the architecture to multi-dimeesional data. JamMa (54] proposes Joint Mamba for feature matching. which enables high-freew fighffrequency interactions between feature pairs. Building on these advances, we integrate Mamba into the one-to-many multi-view stereo (MVS) setting to capture long-range dependencies across multi-view features. This novel adaptation is specifically designed to address the unique challenges of MVS.
3 Methodology
3.1 Network Overview
The overall architecture of MVSMamma is depicted in Fig. 2 Given K input images {I+) K-1 x consist of a reference image (k 0) and K - source images (0 < k < K), the goal is estimate depth map for the reference image. We integrate the proposed Dynamic Mamba module (DM- (od-lod into the conventional Feature Pyramid Network (FPN) 12 to capture rongerange dependencies across multi-view features, efficiently aggregating the local features of the FPN encoder into global and omnidirectional features. Then, we perform multi-scale aggregation within the FPN. Finally, we predicted depth map from the FPN output features a coarse-to-fin manner 11 55
e
3.2 Dynamic Mamba Module
The feature aggregation paradigm in existing Transformer-based MVS methods [8] 24 20 typically involves aggregating information from reference feature into source features 188, thereby enabling improved source feature representation. However, this process often requires repeatedly alternating between self-attention and cross-attention computations, making it difficult to achieve favorable balance between performance and efficiency. Therefore, propose Dynamic Mamba module (DM-
3

---

Multi-Scale Feature Extraction with Mamba
Coarse-to to- Fine Depth Estimation
3D U-Net
Warping
Volume Prssin
Ue
Ref. Image
M module
sr
r
Upeempling
3D U-Net
Featume Voltmes
Src. Images
Feature Pyramid Network
U-Net
Cost Volume
Ax
h/2
Lines
Conv
Sdiustive
SSM
Lanaer
Reburr Mesginni
h/2 w
00.
h w/2
MLP
0us
w/2
Lirestr
Silu
Reference- Centered Dynamic Scanning
Fene
Mamba Block x4
Renc
Local Features
Dynamic Mamba module (DM -oouule
Global Features
Figure Overall architecture of MVSMamba. The proposed Dynamic Mamba module (DM-module) is integrated into the FPN (Sec. 3.2). First, a reference-centered dynamic scanning strategy extracts four directional feature sequences, which processed by four independent Mamba blocks. The resulting sequences are then merged back into 2D feature maps. Multi-scale feature aggregation (Sec. 3.3 is subsequently performed. Finally, we predicted depth a coarse-to-fine manner (Sec. 3.4
module) with novel reference-centered dynamic scanning strategy, which efficiently performs both intra- and inter- view omnidirectional global feature aggregation. Specifically, given FPN encoder features {Fi E IR Cx
0, 2, 3]K- K-1 where is the scale index, DM-module leverages dynamic scanning order for feature enhancement.
construct long-range dependencies from reference feature to each source feature, we propose reference-centeree dynamic scanning strategy. As illustrated in Fig. (a), take the s-th scale for example, source features k,s are concatenated around the reference feature For along both horizontal and vertical directions, placing them on the top. bottom, left. and right of the reference feature:
RefferenceCeenteeed Dynamic Scanning.
Fane Fenc
F Fenc
X hr
Fenc
Fene
X k,a
Fenc
Xab k.s
X
(1)
F Penc
0.8
where the concatenated features
hhr di kea kis
E IR
CR
-
aw
are the horizontal-right, horizontal-left
arrangement of features, and X arrangements, respectively.
k.s K, E R
28
are the vertical-top. and vertical-botton g
Based on X f. X/1 k.st X k.s and X k.s. adopt the skip scanning [50] strategy with four different directions. As shown in Fig. 3 (a). these four concatenated features ensure the scanning order from the reference feature to the source feature, thereby capturing global contextual dependencies from reference features to the source feature. In addition to global aggregation within both the reference and source features, the source feature can effectively learn global representation from the reference feature.
Specifically, we perform It order scanning on hrr Wi order scanning on X 7 order scanning on X k,s and Z order scanning on X with a step 2 and a dynamic starting coordinate (hu, wx),
resulting in four directional sequences (S/
R
Hw Cx --0--)-
1==1- each with length of 2
HW
$ M (X/T (ha, wk)), ub (hg, wk)),, St. (x (hy, wx)),
S2 WN (xhl. k (hg, wx)),
$ Z (Xrb,
The starting coordinate (Wi, ) is dynamically updated according the source image index l: and the arrangement type of reference and source features, shonw in Fig. (b). Specifically:

---

5
(2 (h,, Wh)
Source 4)
End
St, = -A (2 Cb.. mc)
Ses =N (he,
Source View
(k
Down
Left scan
Right scan
S 7 (X (ft,, W).)
Source View (k 3)
(a) Scanning Directions
(b) Receptive
Figure 3: Overview of our proposed reference-centered dynamic scanning strategy. (a) Scanning directions of each reference-source feature pairs. (b) Receptive Filed of the reference feature to different source features.
(1,0), j = 0,
h4 (L [ + h2) mod 2, ukk ((j 1) mod! + wy) mod! 2,
(0,0), = 1,
s.t. j= (k 10-1] mod 4, and (hy, wy)
(0,1), j 2,
(1,1), 3.
(3)
These directional sequences are then fed into four independent Mamba blocks 26 establish long-range dependency:
{ Mamba((S,)]
Finally, Multilayer Perceptron (MLP) is introduced to further enhance the global representations of four directional sequences:
(S (S 18 LN(MLP((Š))
Analysis of Dynamic Scanning. With the dynamic scanning different features, get omnidirectional global receptive field, enhancing the features effectively. Here we give a deeper analysis of our design.
Given specific source image k the source features are arranged around the reference feature. The scanning types with different orders and starting coordinates are shown in Fig. 3[). As we can see, the scanning step 2 makes the length of scanned sequences 4 times smaller than the total number of pixels, which is beneficial to improve the efficiency of the proposed MVSMamba. However, for each scanning order, features in most pixels are skipped, resulting in the reference feature. Nonetheless, the different starting coordinates of different scanning orders ensure that all the pixels in the reference feature are scanned, resulting a global receptive field in the reference feature.
smaller receptive field
Though global receptive field in the reference feature is obtained when given a specific source image, the scanning in the reference feature is limited to single direction for each pixel, resulting in lack of omnidirectionality in the aggregated features. Since MVS inherently one-to-many feature matching task, where the reference feature typically needs to be matched with different source features, we can change the scunning direction in the reference feature for different source features, as illustrated in Fig. 3 (b), where 4 source features are available (K 2 5). In our implementation, we choose to dynamically update the starting coordinates for different source features rather than changing the scanning direction directly, which produces an equivalent effect.
5

---

Scan11
Scands
Scaf77
Reference Tmages
SNett
ET-MV
GoMVS
MVSMamba (0urs)
Groud Truth
Figure 4: Qualitative comparison of depth maps in challenging scenarios the DTU evaluation dataset. Our method predicted more accurate depth maps in texture-less and reflection regions.
Feature Merging from Different Sequences. After obtaining the enhanced feature sequences {8,) with long-range dependency, we need to recover the globally omnidirectional feature map for each reference- source feature pair by merging the enhanced features.
First, rearrange the features in {f,} four features X/e X E.
R
20 and
x X Cx 30 3 by inversing the scan operations. The enhanced reference feature F Fenc and source feature k,s is obtained as follows:
W
W
H
H
For
khr b
X
:]
ub
li k.s
23-6 i + X
Keelis
23-9
23-a
23-a
(6)
W
W
H
23-s
X Ml
R
23-a
ub
::: 23
:l + Xxf
ke
23-s :
3.3 Multi-Scale Aggregation
The proposed DM-module takes both the reference-view and source-view features as input and enhances the features across the two views. Though effective, brings some computational overhead. In order to reduce the computational complexity and achieve better efficiency, we further design Simplified DM-module (SDM-module). and use these two modules different scales.
The SDM-module only takes the reference or source feature as input and enhances the feature within the provided view. Since only single-view feature is provided, directly the input feature to produce four sequences similar to Eq. (2). The starting coordinates are obtained using Eq. (3) with 1. After feeding the sequences Mamba blocks (26), the enhanced feature be obtained by inversing the sean operations.
Given the DM-module and SDM-module, only DM-moudle at the 0-th scale. While decoding K-
the enhanced (Fenc) ] k== to multi-scale pyramid features, we insert SDM-module before the output layer for the 1-st scale in the FPN decoder. The output features of the FPN decoder are denoted as {Fdee Cx -
1, 2, 3-
3.4 Learning Depth from FPN Features
Based on the FPN output features, we predicted depth map in coarse-to-fine manner First, source features are warped 9 into the reference view to form feature volumes 19, enabling the construction pairwise reference source feature similarities [56 33]. These feature similarities are then fused into cost volume using attention- based weights [55]. Subsequently, a lightweight 3D U-Net [55] employed for cost volume regularization, followed by a softmax operation generate probability volume. Finally, winner-take-all strategy is used to predict the depth map. For more details about coarse-to-fine depth estimation, please refer to the works in [I] 55). Similar existing
MVS works [55], apply cross-entropy loss at each scale to supervise the probability volume.

---

Table I: Quantitative results of point cloud error and model efficiency the DTU evaluation set with coarse-to- terse-to-ffnee learning-basee based MVS methods. The methods are categorized into three groups (from top to bottom): CNN-based on high-resolution images. To indicate the performance-effiiienng balance, we report the average ranking across six metrics of point cloud error and model efficiency. The best second- and third-best results are marked with colors.
menssformrr-basee and Mamba-based. Methods with denotes trained
Point Cloud Error
Model Efficiency.
Methods
Avg. Rank
Overall
Acc.
Comp.
GPU(G)
Time(s)
Params(M)
CasMVSNet
7.17
0.355
0.324
0.385
4.48
0.18
0.93
UniMVSNet
57
8.83
0.315
0.352
0.278
4.75
0.27
0.93
MVSTER 55
5.17
0.303
0.340
0.266
2.70
0.07
0.98
GeoMVSNet
8.00
0.295
0.331
0.259
5.21
0.19
15.31
DMVSNet
8.83
0.305
0.338
0.272
4.01
2.67
GoMVS 29
7.67
0.287
0.347
0.227
12.1
0.64
1,50
TransMVSNet [8
7.83
0,305
0.321
0,289
3.69
0.70
1.15
ET-MVSNel 24
5.00
0.291
0.329
0.253
2.91
0.16
1.09
MVSFormer*
6.17
0.289
0.327
0.251
3.66
0.24
28.01
MVSFormer++
20
5.83
0.281
0.309
0.252
4.71
0.23
39.48
MVSMamba (Ours)
3.83
0.287
0.314
0.260
0.11
1.31
MVSMamba* (Ours*)
2.50
0.280
0.308
0.252
2.82
0.11
1.31
4 Experiment
4.1 Datasets
We conduct experiments three of the most widely used datasets in the field of MVS. (1) DTU (58) an indoor dataset captured under controlled laboratory conditions, consisting of 128 scenes. Each scene is captured under seven different lighting conditions with either 49 or 64 images. Following the MVSNet [9] protocol, we split the dataset into training, validation, and evaluation sets, resulting in total of 27,097 training samples. DTTU used for both training and evaluation. (2) Tanks-and- Temples (59] is a large-scale benchmark captured real-world environments, containing 14 indoor and outdoor scenes. The dataset is divided into a intermediate set and advanced set based on reconstruction difficulty, and is used to evaluation the generalization ability of MVS methods. (3) BlendedMVS is large-scale synthetic dataset MVS training only, comprising 106 training scenes and 7 validation scenes.
4.2 Implementation Details
MVSMamba is implemented using PyTorch [60] and optimized with the Adam optimizer 61
Following common practice [55] 30 20 the model is first trained and evaluated on the DTU [58
dataset. To adapt model to real-world scenes, the DTU-trained model is fine-tuned on
BlendedMVS [2] dataset before evaluation on the Tanks-and Templess benchmark [59]. The final
reconstructed point clouds are obtained using the dynamic fusion strategy [32].
For DTU training, we use 5-view input images at resolution of 512 x 640, with a batch size of 4 for 15 epochs. The initial learning rate is set 0.001 and is halved the 10-th, 12-th, and 14-th epochs. For fine-tuning on BlendedMVS, we use 11-iew images at resolution of 576 x 768 with batch size of for 15 epochs. The initial learning rate is 0.0005 and reduced by half at the 6-th, 8-th, 10-th, and 12-th epochs. Additionally, consistent with [55] 30 20 we conduct high-resolution training on DTU using 5-view images at 1024 1280 resolution for epochs, with an initial learning rate of 0.001, halved at 6-th, 8-th, and 9-th epochs. The number of inverse depth hypotheses in four coarse-to- fine scales set to 32-16-8-4 with corresponding depth intervals 2-1-1-0.5, and the group correlation of 4-4-4-4.
7

---

F-score-65.85
6-score-68.79
F-score=19.23
F-score=69.66
F-score=72.90
2t
3r
TVasnMV SNet
SNet
GoMVS
MVSFomee++
MVSMamba(Ours)
Figure 5: Qualitative comparison of reconstructed point clouds on the Tanks-and- benchmark. The top row shows the precision of Francis (T = 5mm) from the intermediate set, while the bottom row presents the precision of Ballroom 10mm) from the advanced set. Brighter regions indicate lower reconstruction errors under the corresponding distance threshold т.
Table 2:
Quantitative results the Tanks-and-Temen Temples benchmark with F-score [%]. The mean refers the average F-score of all scenes. Methods are categorized into three groups (from top to bottom): CNN-based, Transformer-basee and our Mamba-based. The best second-best. and third-best results are marked with colors.
Intermediate
Advanued
Mean
Fam.
Fra.
Hlo.
L.H.
M60
Pan.
P.G.
Tru.
Musan
Aud.
Bul.
Cou.
Mus.
Pul.
Tom.
CuMVVNN
76.37
58.45
26
56.11
54.06
49.51
31.12
19.81
43.87
27.36
UniMVSNet 57
61.36
$1.20
66.43
53.11
63:46
66.09
64.84
62.25
57.53
38.96
28.33
64.36
39.74
52.89
33,80
34.63
MYSTER 55
60.92
30.21
63.51
52.30
61.47
58.16
51.38
37.53
26.68
35.65
49.37
32.16
39.19
GeoMVVNNe
27
65.89
81.64
67.53
55.78
68.02
65.49
67.19
63.27
58.22
41.52
30.23
46.54
39.99
53.05
35.95
43.34
DMVSNN
22
64.66
81.27
67.54
59.10
63.12
64.64
64 80
59.83
56.97
41.17
30.08
10
40.65
53.53
35.08
41.60
GoMVS A9
66.41
82.68
69,23
69.19
63.56
65.13
62.10
58.81
60.80
43.07
35.52
47.15
42.52
52,06
36.34
44.82
TranMMVNNel
63.52
30.92
65.83
$6.94
62.54
63.06
60.00
60.20
58.67
37.00
24.84
44.59
34.77
46.49
34,69
36.62
CostFormer
$1.31
65.65
55.57
63.46
66.24
65.39
61.27
57.30
29.43
29.18
45.21
39.88
53.38
34,07
34.87
WT-MVSNet
24
65.34
81.87
67.35
57.76
64.77
08.68
64.61
62.35
58.35
39, 91
29. 20
44.48
39.55
53.49
34.57
38.15
FT-MVSNa
68.79
59.44
64.22
64.03
40.41
28
18
35.30
MVSFommer
33
66.37
82.06
69.34
60.49
68.61
65.67
64.08
61.23
59.53
40.87
28.22
46.75
39.30
52.88
35.16
42,95
MVSFommer++
69.45
64.24
66.43
29.93
33.58
35.56
MVSMambu (Ours)
67.67
82.47
72.90
58.55
69.63
65.34
66.88
65.80
$9.98
43.32
30.95
49.61
41.04
54,92
36.72
16.67
4.3 Benchmark Performance
Evaluation DTU. We use the official evaluation script report three standard metries: accuracy (Acc.), completeness (Comp.), and their average (Overall). Moreover, we also evaluate the model efficiency (GPU memory, runtime and parameters) using 5-view input images with resolution of 832 x 1152 to ensure fair comparison. For the model trained on low-resolution (MVSMamba), we use 5-view input images at resolution of 832 x 1152. For the model trained on high-resolution (MVSMamba*), we use 5-view input images with resolution of 1152 x 1600. The quantitative results of point cloud error and model efficiency are shown in Tab. We compare our method with state-of-the-art coarse-to-fine learning-basse MVS methods. MVSMamba** achieves the highest overall score and accuracy, while also demonstrating the best balance between performance and efficiency. Meanwhile, MVSMamba outperforms all other methods in the performance- efffecinnff trade-off, with performance second only to MVSFormer++
which was trained on high- resolutios images with the lower efficiency. As shown in Fig. 4 our method produces more accurate depth maps in challenging regions, highlighting its robustness and generalization capability.
Evaluation on Tanks-and-Temn Temples. We evaluate our method on the Tanks-and- Templees benchmark to assess its generalization capability, and report the F-score as the metric. Consistency with (30 20). the evaluation is conducted using 21-view input images with 2k resolution. The quantitative results of intermediate and advanced sets are shown in Tab. 2 Our method achieves best performance on both intermediate and advanced sets among all published methods, which demonstrate our powerful generalization capability. Fig. 5 shows the qualitative results of reconstructed point clouds, our method exhibit superior precision. More visualization results are provided in Appendix D
8

---

Table 3: Ablation study of each component in MVSMamba.
Modules
Overall
Acc.
Comp
MAE
GPU(G)
Time(s)
Pa)a
full
0.287
0.314
0.260
5.21
2.82
0.111
1.31
w/o DM
0.295
0.317
0.272
5.58
2.82
0.104
1.15
w/o SDM
0.289
0.317
0.261
5.45
2.82
0.097
1.15
w/o MLP
0.293
0.315
0.271
5.23
2.82
0.108
1.24
Table 4: Comparison of different feature aggregation modules and sean strategies.
Methods
Overall
Acc.
Comp.4
MAEI
GPU(G)
Time(s))
Params(M)
w/o Aggregation
0.305
0.315
0.295
6.12
2.78
0.09
0.98
DCN 13
0.295
0,310
0.280
5.84
4.35
0.55
1.65
w/ FMT 18
0.296
0.311
0.281
5.93
2.85
0.19
1.27
w/ ET 24
0.291
0.310
0.272
5.62
2.91
0.17
1.09
w/ MMmbb
49
0.291
0,310
0.272
5,30
2.82
0.13
1.31
EVMamba
0.298
0.320
0.276
5.81
2.82
0.11
1.31
w/ JamMa
0.301
0.318
0.284
6.01
2.82
0.11
1.31
MVSMamba
0.287
0.314
0.260
5.21
2.82
0.11
1.31
4.4 Ablation Study
We conducted ablation study (more in Appendix C to analyze the effectiveness and efficiency of the proposed module using the metrics reported in Tab. [ along with additional depth metric, Mean Absolute Error (MAE). Unless otherwise specified, we use the model trained on DTU 58 low- resolution, evaluated with 5-view images resolution of 832 1 1152, with all other hyperparameters kept consistent. Since the point cloud metrics on the DTU dataset are highly sensitive to the depth provide additional quantitative ablation studies on detailed
fusion method and its hyperparameters, depth metrics in Appendix C.2 to further validate the effectiveness of our method.
ablate each component of our proposed method. The DM module contributes the most, as it capture both intra- and inter-view long-range dependencies between reference and source features the bottom level of the FPN, and decodes them into all subsequent scales. The SDM modules perform self-feature enhancement at higher levels, strengthening multi-scale interactions and further improving performance. The MLP enhances the feature representations produced by Mamba blocks with only slight increase in parameter count. All three modules incur minimal computational overhead, making our method highly efficient.
Effectiveness of Each Component. As shown in Tab.
Different Feature Aggregation Modules and Scan Strategies. As shown in Tab, 4 row 2-4, we compare our method with three feature aggregation module: DCN [15] 13 18], FMT (18), and ET [2]] Our method achieves the best performance improvement with minimal cost in memory and runtime, and only modest growth in parameter count. As shown in Tab. row 5-7, we compare our reference-centere centered dynamic scan strategy with three scan strategy: the four- nur-directional scan used in VMamba[49) the skip scan used in EVMamba (50), and the joint scan used in JamMa [54]. Our proposed scan strategy achieves the best performance while maintaining the highest efficiency.
Multi-Scale Aggregation. We conducted ablation study on multi-scale aggregation evaluate the impact of applying the DM-module and SDM-module at different scales. As shown in Tab. simply increasing the number of application scales for the DM-module or SDM-module does not yield further performance gains. This is because the DM-module, operating at the coarsest O-th scale, already captures effective intra- and inter-view interactions. These interactions are then propagated through the decoder to all scales. Meanwhile, the SDM-module serves complement to the DM-module, providing felf-feature enhancement. Therefore, given that the DM-module is applied at the 0-th scale, the SDM-module is applied at the 1-st scale instead.

---

Table 5: Ablation study on multi-scale aggregation DM and SDM modules across FPN scales.
Modules
Overall
Acc.
Comp.
MAE
GPU(G)
Time(s))
Params(M))
DM (s=0)
0.289
0.317
0.261
5.34
2.82
0.10
1.15
DM (s=0.1)
0.295
0.320
0.270
5,41
2.82
0.11
1.20
DM (s=0,1,2)
0.292
0.320
0.294
5.38
2.82
0.17
1.21
DM (s=0.1.2.3)
0.294
0.318
0.270
5.49
2.82
0.31
1.22
DM (s=0) SDM (s=1)
0.287
0.314
0.260
5.21
2.82
0.11
1.31
DM (s=0)+ SDM 1,2)
0.293
0.316
0.270
5.27
2.82
0.16
1.31
DM (s=0) SDM (s=1,2,3)
0.296
0,318
0.274
5.35
3.38
0.35
1.31
Table 6: Ablation study different feature concatenation.
Methods
Overall(mm)
Acc.(mm)) Comp.(mm))
MAE(mm)
Source-centered static
0.300
0.318
0.282
5,42
Reference--enteree static
0.294
0.310
0.278
5.28
Source-centered dynamic
0.296
0.312
0.280
5.39
Reference-centered dynamic
0.287
0.314
0.260
5.23
Table 7: Ablation study on weight sharing.
Sharing
Overall(mm))
Acc.(mm)
Comp.(mm) MAE(mm)l
Params(MD)
0.289
0.314
0.264
5.36
1.21
X
0.287
0.314
0.260
5.21
1.31
Different Feature Concatenation Methods. As shown in Table conducted ablation study on four feature concatenation scanning methods: source-centered static, reference--cntered static, source-centered dynamic, and reference-centered dynamic. The results show that proposed reference-centered dynamic method achieves the best performance across both point cloud and depth metrics. We attribute this superior performance to the source features' ability learn consistent global representations from the reference feature (Appendix D.I.
Weight Sharing. As shown Tab. 7 we conducted an ablation study to determine whether the four Mamba modules should share weights. Using four independent Mamba modules (with out weight sharing) achieves better performance. Due Mamba's efficiency, this configuration with only 0.IM increase in the parameter count. This indicates that independent Mamba modules allow different scanning directions to learn distinct information from the sequence, thereby improving model performance.
5 Conclution
In this paper, we present a Mamba-based MVS network, termed MVSMamba, which efficiently aggregates global and omnidirectional feature representations. Specifically, we propose DM-module with novel reference-centers dynamic scanning strategy. This strategy enables anisotropic scanning from the reference feature to the source feature, where the scanning direction is dynamically updated based the index of each source view to achieve omnidirectional coverage. The DM-module is integrated into the FPN to facilitate multi-scale feature aggregation. Experimental results demonstrate that our method outperforms state-of-ther methods on multiple datasets while maintaining superior efficiency.
Acknowledgments. This work was supported by the Beijing Natural Science Foundation (No. 1.257003), National Natural Science Foundation of China (No. 62402042 and 62227801) and Fundamental Research Funds for the Central Universities (No. FRF-TP-25-033..
10

---

References
[1] H. Liu, H. Yu, J. Jiang, Q. Liu, J. Chen, and H. Ma, "Instdrive: Instance-aware 3d gaussian splatting for driving scenes," arXiv preprint arXiv:2508. 12015, 2025.
[2] H. Yu, Q. Liu, H. Liu, Jiang, J. Lyu, J. Chen, and H. Ma, "Xyzeylinder: Feedforward reconstruction for driving scenes based on a unified cylinder lifting method," arXiv preprint arXiv:2510.07856 2025.
[3] S. Galliani, K. Lasinger, and K. Schindler, "Massively parallel multiview stereopsis by surface normal diffusion," in Proceedings of the IEEE International Conference on Computer Vision, 2015. pp. 873-881.
[4] Q. Xu and W. Tao, "Planar prior assisted patchmatch multi-view stereo," in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 07, 2020, pp. 12516-12523.
[5] Q. Xu, W. Kong, W. Tao, and M. Pollefeys, "Multi-scale geometric consistency guided and planar prior assisted multi-view stereo," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 4, pp. 4945 4963-4963,200 2022.
[6] Z. Yuan, Z. Yang. Y. Cai, K. Wu, M. Liu, D. Zhang. H. Jiang, Z. Li, and Z. Wang. "SED- MVS: Eigmentaatio-Drivvar and Edge-Aligned Deformation Multi-View Stereo with Depth Restoration and Occlusion Constraint," IEEE Transactions on Circuits and Systems for Video Technology, 2025.
[7] Z. Yuan, C. Liu, F. Shen, Z. J. Luo, T. Mao, and Z. Wang, "MSP-MVS: Multi-granul segmentation prior guided multi-view stereo," in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, 2025, pp. 9753-9762.
[8] Z. Yuan, J. Luo, F. Shen, Z. Li, C. Liu, T. Mao, and Z. Wang, "DVP-MVS: Synergize depth-edge and visibility prior for multi-view stereo," in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, 2025, pp.
[9] Y. Yao, Z. Luo, S. Li, T. Fang, and L. Quan, "Mvsnet: Depth inference for unstructured multi- view stereo," " in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 767-783.
[10] Y. Yao, Z. Luo, S. Li, T. Shen, T. Fang. and L. Quan, "Recurrent mvsnet for high-resolution multi-view stereo depth inference," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp.
[1I] X. Gu, Z. Fan, S. Zhu, Z. Dai, F. Tan, and P. Tan, "Cascade cost volume for high-resolution multi- view stereo and stereo matching." in Proceedings of the IEEE/CVF conference computer vision and pattern recognition, 2020, pp. 2495-2504.
[12] S.-W. Kim, H.-K. Kook, J.-Y. Sun, M.-C. Kang, and S.-J. Ko, "Parallel feature pyramid network for object detection," in Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 234-250.
[13] Z. Wei, Q. Zhu, Min, Y. Chen, and G. Wang, "Aa-rmvsnet: Adaptive aggregation recurrent multi-view stereo network." " in Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021, pp. 6187-6196.
[14] K. T. Giang, S. Song, and S. Jo. srrvature-guided dynamic scale networks for multi-view stereo," in International Conference on Learning Representations, 2022.
[15] J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei, "Deformable convolutional networks," in Proceedings of the IEEE International Conference Computer Vision, 2017, pp. 773.
[16] S. Wang, B. J. Yang, and Y. Dai, "Adaptive feature enhanced multi-view stereo with epipolar line information aggregation," IEEE Robotics and Automation Letters, 2024.
[17] D. Bahdanau, K. Cho, and Y. Bengio, "Neural machine translation by jointly learning to align and translate," CoRR, vol. hbb//449.0477 2014.
[18] Y. Ding, W. Yuan, Q. Zhu, H. Zhang, X. Liu, Y. Wang, and X. Liu, "Transmvsnet: Global context-aware niew stereo network with transformers." in Proceedings the IEEF/CVF Conference Computer Vision and Pattern Recognition, 2022, pp. 8585-8594.
11

---

[19] J. Jiang, L. Wang, H. Yu. T. Hu, J. Chen, and H. Ma, "Rrt-mvs: Recurrent regularization trans- former for multi-view stereo," in Proceedings of the AAAI Conference on Artificial Intelligence. vol. 39, no. 4, 2025, pp. 4902-4002.
[20] C. Cao, X. Ren, and Y. Fu, "Mvsformer++: Revealing the devil in transformer's details for multi-view stereo," in The Twelfth International Conference on Learning Representations, 2024.
[21] A. Katharopoulos, A. Vyas, N. Pappas, and F, Fleuret, "Transformers are rnns: Fast autore- gressive transformers with linear attention," in International conference on machine learning. PMLR, 2020, pp. 5156-5165.
[22] J. Liao, Y. Ding, Y. Shavit, D. Huang, S. Ren, J. Guo, W. Feng, and K. Zhang, "Wt-mvsnet: window-basee based transformers for multi-view stereo," " Advances in Neural Information Processing Systems, vol. 35, pp. 8564-8576 2022.
[23] Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei. Z. Zhang, S. Lin, and B. Guo, "Swin transformer: Hicrarchical vision transformer using shifted windows," " in Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 012--10022..
[24] T. Liu, X. Ye, W. Zhao, Z. Pan, M. Shi, and Z. Cao, "When epipolar constraint meets non-local operators in multi-view stereo," in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 18 088-18 097.
[25] A. Gu, K. Goel, and C. Ré, "Efficiently modeling long sequences with structured state spaces," arXiv preprint arXiv:2111.003% 2021.
[26] A. Gu and T. Dao, "Mamba: Linear-time sequence modeling with selective state spaces," arXiv preprint arXiv:231 2...32..07555 2023.
[27] 7. Zhang, R. Peng. Y. Hu, and R. Wang, "Geomvsnet: Learning multi-view stereo with geometry perception," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 21 218-21518.
[28] X. Ye, W. Zhao, Liu, Z. Huang. Z. Cao, and X. Li, "Constraining depth map geometry for multi-view stereo: A dual-depth approach with saddle-shaped depth cells." in P Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 17 671-17 670.
[29] J. Wu, R. Li, H. Xu, W. Zhao, Y. Zhu, J. Sun, and Y. Zhang, "Gomvs: Geometrically consistent cost aggregation for multi-view stereo," in Proceedings of the IEEE/CVE Conference on Computer Vision and Pattern Recognition, 2024, pp. 20 207-20216.
[30] C. Cao, X. Ren, and Y. Fu, "Mvsformer: Multi-view stereo by learning robust image features and temperature- based depth," " Transactions on Machine Learning Research, 2022.
[31] J. Zhang, S. Li, Z. Luo. T. Fang, and Y. Yao, "Vis-mvsnet: Visibility-awarc nult stereo network," International Journal of Computer Vision, vol. 131, no. 1, pp. 199-214, 2023.
[32] Yan, Z. Wei, H. Yi, M. Ding, R. Zhang, Y. Chen, G. Wang, and Y.-W. Tai, "Dense hybrid recurrent multi view stereo net with dynamic consistency checking," in European conference on computer vision. Springer, 2020, pp. 674 679.
[33] F. Wang, S. Galliani, C. Vogel, P. Speciale, and M. Pollefeys, "Patchmatchnet: Learned multi- view patchmatch stereo." in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021, pp. 14 194-194444 203.
[34] F. Wang, S. Galliani, C. Vogel, and M. Pollefeys. "Itermvs: Iterative probability estimation for efficient multi-view stereo." " in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 8606-8615.
[35] S. Wang, B. Li, and Y. Dai, "Efficient multi-view stereo by iterative dynamic cost volume," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 8655-8664.
[36] Jiang, M. Cao, J. Yi, and C. Li, "Di-mvs: Learning efficient multi-view stereo with depth- aware iterations." " ICASSP 2024 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3180-3184. 2024.
[37] Jiang and H. Ma, "Efficient multi-view stereo with depth-aware iterations and hybrid loss strategy," Pattern Recognition, p. 112500, 2025.
12

---

[38] J. Yang. W. Mao, J. M. Alvarez, and M. Liu, "Cost volume pyramid based depth inference for multi-view stereo," Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 4877-4886
[39] S. Cheng. Z. Xu, S. Zhu, Z. Li, L.. E. Li, R. Ramamoorthi, and H. Su, "Deep stereo using adaptive thin volume representation with uncertainty awareness," in Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 2524-2534.
[40] J. Jiang, Q. Liu, H. Yu, H. Liu, L. Wang. J. Chen, and H. Ma. "Monomvsnet: Monocular priors guided mult stereo network," in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2025, pp. 27 806-27 816.
[41] X. Chu, Z. Tian, Y. Wang. B. Zhang, H. Ren, X. Wei, Xia, and C. Shen, "Twins: Revisiting the design of spatial attention in vision transformers," Advances in neural information processing systems, vol. 34, pp- 9355-9366 2021.
[42] M. Oquab, T. Darcet, T. Moutakanni, H. V. Vo, M. Szafraniec, V. Khalidov, P. Fernandez,
D. HAZIZA, F, Massa, A. El-Nouby et al., "Dinov2: Learning robust visual features without supervision," Transactions on Machine Learning Research, 2024.
[43] L. Yang, B. Kang, Z. Huang, Z. Zhao, X. Xu, J. Feng, and H. Zhao, "Depth anything v2," Advances in Neural Information Processing Systems, vol. 37, pp. 21 875-21 911, 2024.
[44] Dao, "FlashAttention-2. Faster attention with better parallelism and work partitioning," in International Conference on Learning Representations (ICLR), 2024.
[45] H. Guo, Li, T. Dai, Z. Ouyang, X. Ren, and S.-T. Xia, "Mambair: A simple baseline for image restoration with state-space model," in European conference on computer vision. Springer, 2024, pp. 222-241.
[46] H. Li, Y. Hou, X. Xing, X. Sun, and Y. Zhang, "Occmamba: Semantic occupancy prediction with state space models," 2025 IEEE/CVF Conference Computer Vision and Pattern Recognition (CVPR), pp. 949-19991611 959, 2024.
[47] B. Zou, Guo, X. Hu, and H. Ma, "Rhythmmamba: Fast, lightweight, and accurate remote physiological measurement," in AAAI Conference on Artificial Intelligence, 2024.
[48] L. Zhu, B. Liao, Q. Zhang, X. Wang, W. Liu, and X. Wang. "Vision mamba: efficient vi- sual representation learning with bidirectional state space model," in Proceedings of the 41st International Conference on Machine Learning, 2024, pp. 62 42942262444 442.
[49] Y. Liu, Y. Tian, Y. Zhao, H. Yu, L. Xie, Y. Wang. Q. Ye, J. Jiao, and Y. Liu, "Vmamba: Visual state space model," Advances in neural information processing systems, vol. 37, pp. 103 031-103 063, 2024.
[50] X. Pei, T. Huang, and C. Xu, "Efficientvmamba: Atrous selective scan for light weight visual mamba," in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, no. 6, 2025, pp. 6443-6451.
[51] A. Hatamizadeh and Kautz, "Mambavision: A hybrid mamba-transformer vision backbone," in Proceedings of the Computer Vision and Pattern Recognition Conference, 2025, pp. 25 261- 25 270.
[52] S. Lee, J. Choi, and H. J. Kim, "Efficientvim: Efficient vision mamba with hidden state mixer based state space duality," " in 2025 JEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 14923-14933
[53] S. Li, H. Singh, and A. Grover, "Mamba-nd: Selective state space modeling for multi- dimensional data," arXiv preprint urXiv:2402.05882 2024.
[54] X. Lu and S. Du, "Jamma: Ultra-lightweight local feature matching with joint mamba," 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14934-14999 14943, 2025.
[55] X. Wang, Z. Zhu, G. Huang. F. Qin, Y. Ye, Y. He, X. Chi, and X. Wang, "Myster: Epipolar transformer for efficient multi-view stereo," " in European Conference on Computer Vision. Springer, 2022, pp. 573-591.
[56] Q Xu and W. Tao, "Learning inverse depth regression for multi-view stereo with correlation cost volume," in Proceedings of the AAAI conference on artificial intelligence, vol. 34, no. 07, 2020, pp. 12 1255.
13

---

[57] R. Peng, R. Wang, Z. Wang, Y. Lai, and R. Wang, "Rethinking depth estimation for multi-view stereo: umified representation," " in Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, 2022, pp.
[58] H. Aanæs, R. R. Jensen, G. Vogiatzis, E. Tola, and A. B. Dahl, "Large-seale data for multiple- view stereopsis," " International Journal of Computer Vision, vol. 120, pp. 153-168, 2016.
[59] A. Knapitsch, J. Park, Q.-Y. Zhou, and V. Koltun, "Tanks and temples: Benchmarking large- scale scene reconstruction," " ACM Transactions on Graphics (ToG), vol. 36, no. 4, pp. 1-13, 2017.
[60] A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, 7. Lin, N. Gimelshein, I.. Antiga al., "Pytorch: An imperative style, high-perfommance deep learning library," Advances in neural information processing systems, vol. 32, 2019.
[61] D. P. Kingma and J. Ba. "Adam: A method for stochastic optimization." " arXiv preprint arXiv:1412.6980, 2014.
[62] Y. Yao, Z. Luo. S. Li, J. Zhang, Y. Ren, L. Zhou, T. Fang, and L. Quan. "Blendedmvs: A large-scale dataset for generalized multi-view stereo networks," " in Proceedings of the IEEF/CVF conference on computer vision and pattern recognition, 2020, pp. 1790-1799.
[63] W. Chen, H. Xu, 7.. Zhou, Y. Liu, B. Sun, W. Kang, and X. Xie, "Costformer: cost transformer for cost aggregation multi-view stereo," in Proceedings of the Thirty-Second International Joint Conference Artificial Intelligence, 2023, pp. 599-608.
[64] R. L. Williams, D. A. Lawrence et al., Linear state-space control systems. John Wiley & Sons, 2007.
[65] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, t.. Kaiser, and I. Polosukhin, "Attention is all you need," Advances in neural information processing systems, vol. 30, 2017.
[66] T. Schops, J. L. Schonberger, S. Galliani, T. Sattler, K. Schindler, M. Pollefeys, and A. Geiger, "A multi-view stereo benchmark with high-resolution images and multi-camera videos," " in Proceedings of the IEEE conference computer vision and pattern recognition, 2017, pp.
[67] S. Wang, V. Leroy, Y. Cabon, B. Chidlovskii, and J. Revaud, "Dust3r: Geometric 3d vision made easy." 2024 IEEE/CVF Conference on Computer Vision and Puttern Recognition (CVPR), pp. 20 697-20 709, 2023.
[68] V. Leroy, Y. Cabon, and J. Revaud, "Grounding image matching 3d with mast3r," in European Conference on Computer Vision, 2024.
[69] J. Wang. M. Chen, N. Karaev, A. Vedaldi, C. Rupprecht, and D. Novotný, "Vggt: Visual geometry grounded transformer." 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5294-5306, 2025.
14

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: The abstract and introduction clear claims we propose novel Dynamic Mamba module for Multi-Vin View Stereo.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or
NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
is fine to include aspirational goals as motivation as long as is clear that these goals are not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations the work performed by the authors?
Answer: [Yes]
Justification:
We discuss the limitations in Appendix
B
Guidelines:
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create a separate "Limitations" section their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution is low images are taken in low lighting. Or speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.
While the authors might fear that complete honesty about limitations might used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't 't acknowledged judgment and recognize that individual actions in favor of transparency play impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
the paper. The authors should use their best
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [No]
15

---

Justification:
This paper does not involve theoretical research.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated referenced in the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but they appear in the supplemental material, the authors are encouraged to provide short proof sketch provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that affects the main claims and/or conclusions of the paper (regardless whether the code and data are provided or not)?
Answer: [Yes]
Justification:
The experiment details see Sec 4.2
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g.. in the case of large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
While NeurIPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) If the contribution is primarily a new algorithm, the paper should make clear how to reproduce that algorithm. (b) the contribution is primarily a new model architecture, the paper should describe the architccture clearly and fully.
(c) If the contribution is new model (e.g., a large language model), then there should either way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reprodueibility. the case of closed-source models, may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers have some path to reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material?
16

---

Answer: [No]
Justification:
The code will be release after the paper is accepted.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurIPS code and data submission guidelines (https://nips.cm
for more details.
While we encourage the release of code and data, we understand that this might not be possible, so "No" an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines (https
/ /pipp/.ccppullc//uuuii wwwti//gguie///eeeeceoocc CoodeSubeiiieeee ssi wsionnolli onnolicy)))) for more details.
The authors should provide instructions on data access and preparation, including how access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification:
The experiment details see Sec 4.2
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core of the paper to level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, or as supplemental material.
7. Experiment statistical significance
Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Answer: [No]
Justification:
We report metrics based on the average results of multiple experiments.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by error bars, confi- dence intervals, statistical significance tests, at least for the experiments that support main claims of the paper.
The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, overall run with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call a library function, bootstrap, etc.)
The assumptions made should be given (e.g.. Normally distributed errors).
should be clear whether the error bar is the standard deviation the standard error of the mean.
17

---

It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of enrors is not verified.
For asymmetric distributions, the authors should be careful to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative eтror rates).
If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of exccution) needed to reproduce the experiments?
Question:
Answer: [No]
Justification: We use NVIDIA RTX A6000 GPUs for tranining and NVIDIA RTX 3090 for evalution.
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported the paper (e.g., preliminary or failed experiments that didn't make into the paper).
9. Code ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https: htttp::/nneurpps http:rpppp..ppplloo/c/cceeeeeraa
Answer: [Yes]
Justification:
This papaer adheres to the NeurlPS Code of Ethics.
Guidelines:
The answer NA means that the authors have not reviewed the NeurlPS Code of Ethics.
If the authors answer No, they should explain the special cireumstances that require deviation from the Code of Ethics.
The authors should make to preserve anonymity (e.g.. if there is special consid- cration due to laws or regulations their jurisdiction..
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification: This work will have an impact on the field of MVS.
Guidelines:
The answer NA means that there is no societal impact of the work performed.
If the authors answer NA or No, they should explain why their work has no societal impact why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
18

---

The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms monitor how system learns from feedback over time, improving the efficiency and accessibility of MI..
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification:
There are no security issues in the implementation of this work.
Guidelines:
The answer NA means that the paper poses such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Lieenses for existing assets
Are the creators or original owners of assets (e.g., code, data, models), used in the paper. properly credited and are the license and terms of use explicitly mentioned and properly respected?
Question:
Answer: [Yes]
Justification: All datasets (c.g., DTU) and code bases properly cited.
Guidelines:
The answer NA means that the paper does
existing assets.
The authors should cite the original paper that produced the code package or dataset.
The authors should state which version of the asset is used and, if possible, include URL.
The name of the license (c.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (c.g., website), the copyright and terms of service of that source should be provided.
If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswi thcode. com com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.
19

---

If this information is not available online, the authors are encouraged to reach out to the asset's creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [No]
Justification: This work does not introduce any new datasets.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details of the dataset/code/mmoel as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent obtained from people whose asset is used.
At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
14. Crowdsoureing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Answer: [NA]
Justification: The work does not involve crowdsourcing or human participants
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included the main paper.
According the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question:
Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]
Justification:
The study does not involve human subjects or sensitive data requiring IRB
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Depending the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and expect authors to adhere to the NeurlPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such the institution conducting the review.
16. Declaration of LLM usage
20

---

Question: Does the paper describe the usage of LLMs if it is important, original, or non-standard component of the core methods this research? Note that if the LLM used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.
Answer: [NA]
Justification: LLMs not used part of the core methodology in this work
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs any important, original, non-standard components.
Please refer to our LLM policy for what should or should not be described.
21

---

A Mamba
A.1 State Space Models
State Space Models (SSMs) are originally designed to model continuous linear time-invariant
systems 64 These models map an input signal stt) to an output y(t) via a hidden state h(t)
as:
(1(1) - Ah(() + Bz((), v(t) = Ch'(
(7)
where A E RRNN, , B and C E RIXN are system parameters. To enable the application of
SSMs in discrete sequence modeling tasks, such as teeeuce-o-ssqueece learning. S4 [25] discretizes these parameters using the zero-order hold method. However, S4 shares parameters across all time steps, which limits its expressiveness in complex sequential contexts.
A.2 Mamba Module
To address the limitations of S4, Mamba (26 introduces a refined formulation named S6, where the SSM parameters and C are made input-dependent. This dynamic parameterization allows Mamba to adaptively modulate state transitions based on the input sequence, significantly enhancing its representation power and enabling performance on par with Transformer models [65] Moreover, Mamba achieves high efficiency by reformulating the recurrent SSM computation into a single global convolution operation. Specifically, a convolution kernel is precomputed, allowing output computation as:
K = (CB, CAB,.... CAN
y == K,
(8)
where * denotes the convolution operator. This structure supports both dynamic modeling and fast parallel training.
B More Quantitative Results
B.1 Evaluation on ETH3D
ETH3D [66] benchmark contains high-resolution images with significant viewpoint transformations. We adopt an automatic evaluation process by uploading the generated point clouds to the official website. This process measures the accuracy (Acc.) and completeness (Comp.) of the generated point clouds. The F-score is defined as the harmonic mean of Acc. and Comp. We evaluate MVSMamba on the ETH3D training benchmark using the model finetuned BlendedMVS (2). with the number of input views set to 11 and the image resolution to 1600 2432. However, point cloud fusion involves complex post-processing steps, requiring careful, per-scene hyperparameter selection to improve metrics. For fair comparison, we follow the approach of MVSFormer++ [20]. We adopt the default dynamic fusion strategy B2 and set the depth confidence filtering threshold to 0.5 for all subscenes, without any hyperparameter tuning. As shown Tab. 8 MVSMamba achieved competitive performance with MVSFormer++, while also realizing a 52.1% reduction in running time and 28.5% reduction in GPU memory consumption, thanks to the DM-modulc's efficient multi-view global feature representation. In contrast, Transformers result impractically high complexity when processing such high-resolution images.
Table 8: Quantitative results on the ETH3D benchmark.
Methods
Acc,
Comp
F-score(96)
Time(s))
Mcmory(G),
MVSFormer++
20
81.88
83.88
82.99
2.11
9.31
MVSMamba (Ours)
87.87
76.85
81.69 (-1.5%)
1.01 (-52.1%)
6.65 (-28.5%)
B.2
Comparison with Feed-Forward MVS on DTU
DUSt3R (67
series of feed-forward MVS methods (such as MASt3R (8 and VGGT (69] are trained on diverse datasets containing millions of images and perform 3D reconstruction without
22

---

known Ground-Trun (GT) cameras. In contrast, MVSNettbasse 9 methods (such MVSMamba and MVSFormer++) are trained solely on the DTU and BlendedMVSS datasets and require known GT cameras to construct cost volumes. Due to these fundamental differences, these two categories of methods are not directly comparable. Tab, rab.fJnonetheless presents direct performance comparison on DTU. MVSMamba significantly outperforms feed-forward methods that operate without known GT cameras (DUSt3R, VGGT), as well as MAS(3R, which triangulates matches using known GT cameras to derive depth maps.
Table 9: Quantitative comparison with feed-forward MVS methods on the DTU dataset.
Methods
Known GT camera
Overall(mm))
Acc.(mm))
Comp.(mm)
DUS(3R 67
1.741
2.677
0.805
VGGT 69
X
0.382
0.389
0.374
MAS03 68
0.374
0.403
0.344
MVSMamba(Ours)
0.280
0.308
0.252
C Additional Ablation Study
C.1 Loss Function
As shown in Tab. 10 Cross-Entropy (CE) loss significantly outperforms L1 loss on all point cloud metrics, while the difference in depth metrics is minimal. This is because CE loss directly supervises the probability volume, yielding more reliable confidence maps that are crucial for the subsequent depth map fusion process.
Table 10: Ablation study on loss function.
Loss
Overall(mm)) Acc.(mm))
Comp-
MAE(mm)
L
0.302
0.319
0.285
5.21
CE
0.287
0.314
0.260
5.21
C.2 Ablation on Depth Metrics
Consistent with the settings in the main paper Sec. 4.4 we conducted detailed ablation studies on depth metrics to further validate our method's effectiveness. These metrics include Mean Absolute Error (MAE). Root Mean Square Error (RMSE), and depth precision (Prec.) at thresholds l 2mm, and 4mm. All metrics were evaluated a resolution of 832x 1152 on DTU [58] Tab. [T shows the effectiveness of each component. Tab. T2 compares different feature aggregation modules and scanning strategies. Tab. [3] compares different feature concatenation methods. Tab. [4 evaluates the impact of weight sharing among the four Mamba modules. Tab. 15 compares the performance of different loss functions.
Imm,
Table 11: Ablation study of each component in MVSMamba.
Modules
MAE(mm)
RMSE(mm))
Prec. Imm(%)
Prec. 2mm(%)t
Prec. 4mm(%)
full
9.59
27.78
66.01
78.06
84.09
w/o DM
11.29
30.72
64.16
76.46
82.58
w/o SDM
10.63
30.41
66.75
78.54
84.34
w/o MLP
8.72
26.04
65.58
77.89
83.96
C.3 More Input Views
The DM-module adopts reference-centerel scanning strategy, allowing the reference features to fully leverage multi-view information for learning global and omnidirectional feature representations.
23

---

Table 12: Comparison of different feature aggregation modules and sean strategies.
Methods
RMSE(mm)) MAD(mm)J
Prec. Imm(%)t
Prec. 2mm(%))
Prec. 4mm(%%)
w/o Aggregation
10.31
30.06
63.28
76.29
82.79
DCN 11
11.73
32.83
65.01
77.11
82.88
w/ FMT 18
17.83
45.98
64.01
75.50
80.89
w/ ET 24
13.24
35.34
65.52
77.33
83.08
w/ VMamba 49
11.58
30.83
65.63
77.57
83.39
w/ EVMamba 50
12.59
33.78
64.91
77.16
83.06
w/ 54
15.89
40.08
57.53
71.95
79.49
MVSMamba
9.59
27.78
66.01
78.06
84.09
Table 13: Ablation study of different feature concatenation.
Methods
MAF(mm)
RMSF(mm))
Prec. Imm(%)f
Prec. 2mm(Se)f
Prec. 4mm(5))
Source-centered static
12.11
33.09
64.07
76.90
82.97
Reference--entered static
9.97
27.97
63.93
76.90
83.15
Source-centered dynamic
10.21
28.67
64.54
76.99
83.22
Reference-centered dynamic
9.59
27.78
66.01
78.06
84.09
Table 14: Ablation study on weights sharing.
Sharing
RMSF(mm)L MAE(mm)4
Prec. Imm(%))
Prec. 2mm(
Prec. 4mm(%)f
10.67
29.74
66.00
77.88
83.76
X
9.59
27.78
66.01
78.06
84.09
Table 15: Ablation study on different loss function.
Loss
MAE(mm))
RMSF(mm))
Prec. Imm(%))
Prec. 2mm(%)f
Prec. 4mm(%))
L1
9.27
25.64
67.60
78.01
83.60
CE
9.59
27.78
66.01
78.06
84.09
Table 16: Ablation study of the total number of training and testing views (reference and source views) on the Banks-and-Templee (59) benchmark.
Intermediate Piscore [%6
Advanced Fuscore [%]
Train
Test
65.00
81.07
68.00
64.88 84
64.58
57.67
24.60
36.96
36.50
41
21
66.46
82.01
72.30
52.89
69.49
64.29
65.98
65.58
$9.08
42.27
28.84
48.98
39.73
53.87
36,80
45.42
67.67
82.47
72.90
58.55
69.63
65.34
66.88
65.60
39.98
43.32
30.95
49.61
41.04
54.92
36.72
46.67
To assess how our method benefits from the number of input views processed by the DM-module, we conduct an ablation study by varying the number of input views during both training and testing. As shown in Tab. [6 the performance consistently improves with more input views both training and testing stages. The 20 candidate souree views are extended by MVSFormer [30].
D More Visualization Results
D.I PCA Features
We apply Principal Component Analysis (PCA) to reduce the number of feature channels to three and visualize the results with RGB. As illustrated in Fig. 6 present the evolution of each reference-

---

source feature pair in the Courtroom scene from the Bonks-and-Tenae Advanced set. The results show that all source features effectively learn consistent global representations from the reference feature, thereby facilitating more reliable subsequent feature matching.
D.2 All Point Clouds
As shown Fig. 7 and Fig. we visualize the reconstructed point clouds on the DTU Tanks-and and-Temples [59 benchmark, respectively.
58 and
E
Limitations
The proposed DM-module and SDM-module are effective when applied at specific FPN scales, simply extending them to FPN encoder features across multiple scales does not yield additional performance gains, indicating that the full potential of Mamba is not yet fully leveraged. Although the FPN structure allows global features to propagate from coarse to fine scales, this process inevitably introduces information loss. Developing a feature interaction framework that supports efficient multi-scale Mamba-base based interaction remains promising direction for future work.
25

---

Reference Sourcel
Reference Source2
Reference Source3
Reference Sourre4
Input Images
Original FPN
FPN+DM (Ours)
Figure 6: We show the PCA features of each pair of reference-source features the Courtroom scene the Tanks-and Temples [59 benchmark at the ()-th scale. For each pair, the top row displays the reference feature, while the bottom shows the corresponding source feature. The source features are able to learn consistent global representations from the reference feature.
26

---

10
TUBORG
Figure 7: All reconstructed point clouds on the DTU 58 dataset by the proposed method.
27

---

Family
Francis
Horse
Light House
M60
Panther
Playground
Train
Auditorium
Ballroom
Courtroom
Museum
Palace
Temple
Figure 8: All reconstructed point clouds on the Tanks-an aed-Temples method.
59 benchmark by the proposed
28


Reviews:
[{"review_id": "kFMXDjlUXF", "reviewer": "Reviewer_A2fC", "summary": "This paper proposes a learning-based MVS method that utilizes the Mamba architecture. As the original Mamba does, the proposed method replaces the Transformer to be more efficient and faster. Additionally, the method includes a scanning strategy that considers multi-view images. Based on the features extracted from multiple Mamba modules, the depth is estimated coarse-to-fine and is trained with cross-entropy loss. The evaluation is done in the DTU and T&T datasets, showing competitive result metrics.", "questions": "I expect the authors to answer the rebuttal for the weakness, along with the following considerations: I'm still not convinced of the meaning of omnidirectional in the paper. It seems the omnidirectional means multiple scanning directions for rearranging image patches for Mamba input, not related to the view directions or other directional components. If so, it causes confusion for some authors, and I recommend replacing it for clarity. \n\nMinor typo:\n- [17] MVSformer++ reference missing authors", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "The paper shows one way to utilize the latest architecture for MVS. Since this is an initial attempt at using Mamba for the MVS problem, it can be shown as the paper's contribution. The proposed Dynamic Mamba module, including the scanning method, seems novel. However, its key idea is adopted from the existing Mamba-based computer vision papers (VMamba, MambaVision, EfficientViM, Mamba-ND, ...), and the remaining contribution is an extension to the multi-view images. As a result, it seems the contributions are relatively weak.\nExcept for the contribution part, the paper provides sufficient details of experiments and information. The evaluation result seems to outperform/comparable to the latest works, and ablation studies seem reasonable.", "was_revised": true, "final_justification": "The author's rebuttal clarified my concerns. And the answers to the questions that the other reviewers raised also seem meaningful. Thus, I keep my ratings.", "rebuttal": "We thank the reviewer for the constructive comments. We provide our feedback as follows.\n\n> **Q1: The proposed Dynamic Mamba module, including the scanning method, seems novel. However, its key idea is adopted from the existing Mamba-based computer vision papers (VMamba, MambaVision, EfficientViM, Mamba-ND, ...), and the remaining contribution is an extension to the multi-view images. As a result, it seems the contributions are relatively weak.**\n\n**A1:** We agree that our work builds upon the foundational success of Mamba in computer vision, a field pioneered by methods like VMamba [1]. Indeed, VMamba is instrumental in adapting Mamba for computer vision by introducing four-directional scanning to bridge the gap between 1D sequences and 2D images. Subsequent works, such as MambaVision [2], EfficientViM [3], and Mamba-ND [4], further explore this space by combining Mamba with self-attention, reducing computational costs, or extending the architecture to multi-dimensional data. These methods are designed for individual features and do not establish connections between multi-view features.\n\nHowever, our contribution is not a straightforward extension but a novel adaptation specifically designed for the unique challenges of Multi-View Stereo (MVS). Unlike the general-purpose methods mentioned, our work is the first to integrate the scanning mechanism with the specific characteristics of MVS feature matching. The core of our novelty lies in the Dynamic Mamba module (DM-module) and its reference-centered dynamic scanning strategy. This strategy is tailored for the \"one-to-many\" feature matching paradigm inherent in MVS. By concatenating a source feature around the reference feature and dynamically adjusting the scan directions for different source views, our approach facilitates rich and efficient intra- and inter-view feature interaction from the reference to the source views. This problem-specific design enables highly efficient feature aggregation.\n\nWe include a more detailed discussion of other computer vision Mamba papers in the final version.\n\n> **Q2: I'm still not convinced of the meaning of omnidirectional in the paper. It seems the omnidirectional means multiple scanning directions for rearranging image patches for Mamba input, not related to the view directions or other directional components. If so, it causes confusion for some authors, and I recommend replacing it for clarity.**\n\n**A2:** We understand the concern regarding the meaning of \"omnidirectional\" in the paper. The term refers to the multiple scanning directions used to rearrange image patches for Mamba's input, rather than being related to camera viewpoints or other directional components.\n\nThe impression that our method involves \"rearranging\" image patches is correct in a sense. This perspective arises from imagining a predefined indexing of patches (e.g., numbering them from top-left to bottom-right) and then creating different sequences by shuffling these indices.\n\nHowever, we clarify a nuance in our approach. Rather than reordering a fixed set of indices, our method directly changes the scanning path across the 2D feature map itself. The original Mamba architecture is designed for unidirectional 1D sequences. To make it understand 2D images, it is necessary to convert 2D features into 1D sequences. Our \"omnidirectional scanning\" achieves this by performing four independent scans from the four cardinal directions (i.e., top-to-bottom, bottom-to-top, left-to-right, and right-to-left), which generates four distinct 1D sequences. Because each path represents a different fundamental direction of traversal, we describe the process as \"omnidirectional.\" This allows the model to perceive and aggregate features from all directions, effectively bridging the gap between a 1D sequence model and 2D spatial information.\n\nThis principle of adjusting the information processing order to suit a specific task is common in computer vision. For example, while a standard Vision Transformer [5] processes patches in a linear sequence, the Swin Transformer [6] introduces \"shifted windows\" to selectively reorganize the interaction scope of patches, which allows it to capture richer contextual information and achieve superior results. Analogously, our method is a novel mechanism, specifically designed for the MVS task, that uses this omnidirectional scanning to enable Mamba to efficiently aggregate global multi-view features.\n\n[1] Liu, Yue, et al. \"Vmamba: Visual state space model.\" NeurIPS 2024.\n\n[2] Hatamizadeh, Ali, et al. \"Mambavision: A hybrid mamba-transformer vision backbone.\" CVPR 2025.\n\n[3] Lee, Sanghyeok, et al. \"Efficientvim: Efficient vision mamba with hidden state mixer based state space duality.\" CVPR 2025.\n\n[4] Li, Shufan, et al. \"Mamba-nd: Selective state space modeling for multi-dimensional data.\" ECCV 2024.\n\n[5] Dosovitskiy, Alexey, et al. \"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.\" ICLR 2021.\n\n[6] Liu, Ze, et al. \"Swin transformer: Hierarchical vision transformer using shifted windows.\" ICCV 2021."}, {"review_id": "Yf9U23OrPe", "reviewer": "Reviewer_iftD", "summary": "The paper proposes a Mamba-based MVS network, to alleviate the quadratic computational complexity of Transformer-based methods while achieving comparable or even outperforming the Transformer-based methods. Specifically, it introduces a Dynamic Mamba Module based on a reference-centered dynamic scanning strategy. The model achieves good results on DTU dataset and Tanks-and-Temples dataset.", "questions": "1. Have you tried any real-world captures (maybe just 1-2 examples) and how the results look like?\n2. It would be helpful to have more analysis on how the proposed modules help in difficult scenarios, e.g. textureless regions, highly-reflected regions etc.\n3. Is there any failure cases and any analysis?\n4. Wondering if you tried using expectation instead of winner-take-all when predicting depth. Also many previous methods e.g. CasMVSNet use L1 loss directly. Have you tried?\n\nI am happy to increase the score if the questions above are addressed properly.", "limitations": "Yes, in the supplementary material.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. It's the first paper to use Mamba in feature extraction for Multi-view Stereo, to consider long-range dependencies with linear time complexity. \n2.  The proposed method achieves good results on both DTU and Tanks-and-Temples when only trained on DTU and finetuned on BlendedMVS.\n\nWeaknesses:\n1. I am wondering if the method can be applied to real-world captures where the poses might have noises (e.g. from COLMAP), and how robust the method can be?\n2. The paper claims the method is both accurate and efficient. While it's easy to understand it's high efficiency, there's no analysis / visualization on how Mamba can help with the accuracy, e.g. for textureless regions, or for failure cases of other methods.\n3. No discussion of failure cases.", "was_revised": true, "final_justification": "I have read reviews from other reviewers. The rebuttal addressed my questions. So I'll keep my score.", "rebuttal": "We thank the reviewer for the constructive comments. We provide our feedback as follows.\n\n> **Q1: I am wondering if the method can be applied to real-world captures where the poses might have noises (e.g. from COLMAP), and how robust the method can be? Have you tried any real-world captures (maybe just 1-2 examples) and how the results look like?**\n\n**A1:** Due to the rebuttal format's limitations, new visual results cannot be submitted at this stage. However, a dedicated section with qualitative results on real-world captures is included in the final version to further demonstrate the method's applicability.\n\n>**Q2: The paper claims the method is both accurate and efficient. While it's easy to understand it's high efficiency, there's no analysis / visualization on how Mamba can help with the accuracy, e.g. for textureless regions, or for failure cases of other methods. It would be helpful to have more analysis on how the proposed modules help in difficult scenarios, e.g. textureless regions, highly-reflected regions etc.**\n\n**A2:** The proposed modules are particularly beneficial in challenging regions (e.g., textureless, reflective) because they provide global contextual information that local CNN features inherently lack. This global context is crucial for disambiguating matches in low-information areas.\nWhile previous methods used computationally expensive Transformers for this purpose, our DM-module leverages Mamba's linear-complexity scanning to achieve efficient global feature aggregation across all views. As qualitatively shown in Figure 1 of our supplementary material, this allows source features to learn globally consistent representations from the reference feature, leading to more reliable feature matching.\nTo make this point more explicit, we will add new visualizations in the final version that directly compare our method with and without the DM-module for these difficult scenarios. This will clearly illustrate our contribution.\n\n> **Q3: No discussion of failure cases. Is there any failure cases and any analysis?**\n\n**A3:** MVSMamba can aggregate global features with extremely high efficiency, but it still cannot effectively handle areas with extreme viewpoint changes or strong occlusions. In these areas, the fundamental geometric correspondence between views is lost,  our global feature aggregation cannot compensate for a complete lack of corresponding visual information. We will add visualizations and related analysis of these failure cases in the final version.\n\n> **Q4: Wondering if you tried using expectation instead of winner-take-all when predicting depth. Also many previous methods e.g. CasMVSNet use L1 loss directly. Have you tried?**\n\n**A4:** We have indeed conducted experiments comparing L1 loss with our current approach using Cross-Entropy (CE) loss. The statistical results for L1 loss and cross-entropy (CE) loss are as follows.\n\n- The first table shows the point cloud metrics and depth metrics (512×640) during the validation process.\n- The second table shows the detailed metrics of the depth map at a resolution of 832×1152.\n\nAs the results show, CE loss significantly outperforms L1 loss in all point cloud metrics, while the difference in depth metrics is minimal. This is because CE loss directly supervises the probability volume, enabling more reliable confidence maps, which are crucial for subsequent depth map fusion processes. Therefore, recent SOTA MVS methods adopt a winner-take-all approach for depth prediction and CE loss for supervising the probability volume (e.g., MVSFormer++ [1] and GoMVS [2]).\n\n| Loss | Overall (mm)↓ | Accuracy (mm)↓ | Completeness (mm)↓ | MAE (mm)↓ |\n| ---- | :-----------: | :------------: | :----------------: | :-------: |\n| L1   |     0.302     |     0.319      |       0.285        |   5.21    |\n| CE   |   **0.287**   |   **0.314**    |     **0.260**      | **5.21**  |\n\n| Loss | MAE (mm)↓ | RMSE (mm)↓ | Precision 1mm (%)↑ | Precision 2mm (%)↑ | Precision 4mm (%)↑ |\n| :--- | :-------: | :--------: | :----------------: | :----------------: | :----------------: |\n| L1   | **9.27**  | **25.64**  |     **67.60**      |       78.01        |       83.60        |\n| CE   |   9.59    |   27.78    |       66.01        |     **78.06**      |     **84.09**      |\n\n[1] Cao, Chenjie, et al. \"Mvsformer++: Revealing the devil in transformer's details for multi-view stereo.\" ICLR 2024.\n\n[2] Wu, Jiang, et al. \"Gomvs: Geometrically consistent cost aggregation for multi-view stereo.\" CVPR 2024."}, {"review_id": "N3nNmr7EDH", "reviewer": "Reviewer_EQ7a", "summary": "In MVS, good feature matching is key, but Transformer-based methods struggle with efficiency due to their quadratic complexity. To solve this, the authors introduce MVSMamba, which uses the Mamba architecture for efficient global feature aggregation with linear complexity.\n\nThe authors propose a Dynamic Mamba (DM) module, which scans features in multiple directions from the reference view. This enables efficient feature interaction across views, omnidirectional multi-view representations, and multi-scale feature aggregation, all while keeping the computational cost low.", "questions": "Given that ETH3D is a standard benchmark in the MVS community, could you clarify why this dataset was not included? Including ETH3D would provide a more comprehensive evaluation of MVSMamba’s generalizability across different datasets.\n\nThe paper does not compare MVSMamba with other recent methods such as DUST3R, VGGT, CUT3R, and FAST3R, which are important works in the MVS field. Could you include a comparison with these methods to better contextualize the performance of MVSMamba?\n\nThe paper appears to combine Mamba with MVSformer++.  I look forward to hearing other reviewers' perspectives and the authors' response regarding the core novelty.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "## Strengths\n\n### Performance: \n\nThe experiments show that MVSMamba outperforms state-of-the-art MVS methods on both the DTU dataset and the Tanks-and-Temples benchmark, excelling in both accuracy and efficiency. The comparison with CNN-based and Transformer-based methods is particularly convincing, with MVSMamba showing the best performance and efficiency across the board.\n\n### Clarity: \n\nFigure 1 effectively illustrates the performance vs. efficiency trade-offs, clearly highlighting the superiority of MVSMamba over existing approaches. The visual comparison between CNN-based, Transformer-based, and Mamba-based methods is straightforward and well-executed.\n\n## Weaknesses\n\n### Limited Dataset Evaluation: \n\nThe paper lacks evaluation on the widely used ETH3D dataset.\n\n### Lack of Comparison with Key Methods: \n\nThere is no comparison with important works such as DUST3R, VGGT, CUT3R, and FAST3R, which would help contextualize MVSMamba’s performance within the current landscape.\n\n### Novelty: \n\nWhile the approach is interesting, the combination of Mamba and MVSformer++ feels incremental.", "was_revised": true, "final_justification": "The rebuttal has addressed my concerns. I have also read the reviews and rebuttals from the other reviewers. It appears that others recognize the contribution of introducing Mamba into MVS, as well as the design of the dynamic Mamba module, as valuable contributions. Based on this, I will raise my rating. I encourage the authors to include results on the ETH3D dataset, along with comparisons to MASt3R, DUSt3R, and VGGT, in the camera-ready version. I also look forward to seeing ETH3D results compared with MASt3R, DUSt3R, and VGGT.", "rebuttal": "We thank the reviewer for the constructive comments. We provide our feedback as follows.\n\n> **Q1: The paper lacks evaluation on the widely used ETH3D dataset. Given that ETH3D is a standard benchmark in the MVS community, could you clarify why this dataset was not included? Including ETH3D would provide a more comprehensive evaluation of MVSMamba’s generalizability across different datasets.**\n\n**A1:** We use the model finetuned on BlendedMVS to evaluate MVSMamba on the large-scale scene dataset ETH3D, which consists of high-resolution images. We set the number of input views to 11 and the image resolution to 1600×2432. However, point cloud fusion involves complex post-processing steps, requiring careful selection of hyperparameters on a per-scene basis to improve metrics. For a fair comparison, we follow the approach of MVSFormer++ [1], adopting the default dynamic fusion strategy and setting the depth confidence filtering threshold to 0.5 for all subscenes, without any hyperparameter tuning. The evaluation results are shown in the table below.\n\n| Methods         | Accuracy (%)↑ | Completeness (%)↑ | F-score (%)↑  |     Time(s)↓      |    Memory(G)↓     |\n| :-------------- | :-----------: | :---------------: | :-----------: | :---------------: | :---------------: |\n| MVSFormer++ [1] |     81.88     |     **83.88**     |   **82.99**   |       2.11        |       9.31        |\n| MVSMamba (Ours) |   **87.87**   |       76.85       | 81.69 (-1.5%) | **1.01** (-52.1%) | **6.65** (-28.5%) |\n\nOur proposed MVSMamba achieved competitive performance to MVSFormer++, with a 52.1% reduction in running time and a 28.5% reduction in GPU memory consumption, thanks to the DM-module's efficient multi-view global feature representation. Transformers result in impractically high complexity when processing high-resolution images.\n\n> **Q2: There is no comparison with important works such as DUST3R, VGGT, CUT3R, and FAST3R, which would help contextualize MVSMamba’s performance within the current landscape. The paper does not compare MVSMamba with other recent methods such as DUST3R, VGGT, CUT3R, and FAST3R, which are important works in the MVS field. Could you include a comparison with these methods to better contextualize the performance of MVSMamba?**\n\n**A2:** DUSt3R [2] series methods are trained on a mixture of various datasets containing millions of images and perform 3D reconstruction without known GT cameras. In contrast, the MVSNet [3] series of methods (such as MVSMamba, MVSFormer++) are trained only on the DTU and BlendedMVS datasets and require known GT cameras to construct cost volumes. Generally speaking, the above two methods cannot be compared fairly.\n\nBelow is a direct comparison of performance on DTU. MVSMamba's performance is significantly better than unknown GT camera methods (DUSt3R, VGGT [4]), as well as MASt3R [5], which derives depth maps by triangulating matches using known GT cameras. CUT3R [6] and Fast3R [7] do not report reconstruction metrics on DTU.\n\n| Methods         | Known GT camera | Overall (mm)↓ | Accuracy (mm)↓ | Completeness (mm)↓ |\n| --------------- | :-------------: | :-----------: | :------------: | :----------------: |\n| MVSMamba (Ours) |        √        |   **0.280**   |   **0.308**    |     **0.252**      |\n| MASt3R [5]      |        √        |     0.374     |     0.403      |       0.344        |\n| DUSt3R [2]      |        ×        |     1.741     |     2.677      |       0.805        |\n| VGGT [4]        |        ×        |     0.382     |     0.389      |       0.374        |\n\n> **Q3: While the approach is interesting, the combination of Mamba and MVSformer++ feels incremental. The paper appears to combine Mamba with MVSformer++. I look forward to hearing other reviewers' perspectives and the authors' response regarding the core novelty.**\n\n**A3:** MVSMamba is not a combination of Mamba and MVSFormer++. Both MVSMamba and MVSFormer++ are built on a pure four-stage coarse-to-fine framework similar to CasMVSNet [8]. Building on this, MVSFormer++ introduces several Transformer-based modules to enhance performance, such as incorporating pre-trained ViT to enhance CNN features, but with lower efficiency. In contrast, MVSMamba is the first model to introduce Mamba into the MVS domain, achieving outstanding performance and efficiency through the design of the dynamic Mamba module (DM-module).\n\n[1] Cao, Chenjie, et al. \"Mvsformer++: Revealing the devil in transformer's details for multi-view stereo.\" ICLR 2024.\n\n[2] Wang, Shuzhe, et al. \"Dust3r: Geometric 3d vision made easy.\" CVPR 2024.\n\n[3] Yao, Yao, et al. \"Mvsnet: Depth inference for unstructured multi-view stereo.\" ECCV 2018.\n\n[4] Wang, Jianyuan, et al. \"Vggt: Visual geometry grounded transformer.\" CVPR 2025.\n\n[5] Leroy, Vincent, et al. \"Grounding image matching in 3d with mast3r.\" ECCV 2024.\n\n[6] Wang, Qianqian, et al. \"Continuous 3d perception model with persistent state.\" CVPR 2025.\n\n[7] Yang, Jianing, et al. \"Fast3r: Towards 3d reconstruction of 1000+ images in one forward pass.\" CVPR 2025.\n\n[8] Gu, Xiaodong, et al. \"Cascade cost volume for high-resolution multi-view stereo and stereo matching.\" CVPR 2020."}, {"review_id": "E0CAJ1GATf", "reviewer": "Reviewer_qVE2", "summary": "This paper proposes MVSMamba , a Mamba-based network for MVS. By leveraging the global modeling capability and linear complexity of the Mamba architecture, MVSMamba introduces a Dynamic Mamba module (DM-module) with a reference-centered dynamic scanning strategy to enable efficient feature interaction. Experimental results show that MVSMamba outperforms state-of-the-art methods on the DTU dataset and Tanks-and-Temples benchmark.", "questions": "1) The baseline without feature aggregation already performs well, and using VMamba instead of your DM-module gives a slightly better score (0.291 vs 0.287). Since the DTU results can be affected by many factors like post-processing, could you provide more detailed depth evaluation metrics (like RMSE or accuracy) to show that your method truly improves performance?\n\n2) The authors concatenate source features around the reference view in four directions. Do these directions consider the actual positions of the views? Have you tested different ways of concatenation to see which one works best?\n\n3) The authors use four independent Mamba blocks for the four directions. Why not share weights between them? Is there any analysis on whether they learn different or similar information?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1) This work introduces MVSMamba , which is among the first attempts to apply the Mamba architecture to the Multi-View Stereo (MVS) task. The method explores the potential of Mamba in modeling long-range dependencies with linear complexity, offering a new direction for global feature aggregation in MVS.\n2) The experiments on the DTU dataset and Tanks-and-Temples benchmark show that MVSMamba achieves competitive performance compared to existing methods, while maintaining relatively low computational cost.\n\nWeaknesses\n1)  As shown in Table 4, the \"w/o aggregation\" baseline already achieves strong performance. Moreover, when alternative feature aggregation methods such as VMamba are applied, the model achieves an overall score of 0.291, which is even slightly better than the proposed MVSMamba's score of 0.287. Given that the overall score on the DTU dataset can be highly sensitive to various factors (including post-processing hyperparameters) it is unclear whether the proposed method truly brings a meaningful improvement. The authors are encouraged to provide more quantitative depth estimation metrics to better justify the effectiveness of MVSMamba.\n2) The module employs directional feature concatenation (e.g., horizontal-right, horizontal-left, vertical-bottom, vertical-top) to facilitate scanning from the reference view to source views. However, it remains unclear whether this design considers the relative spatial positions between views. A quantitative analysis comparing different concatenation strategies and their impact on performance would strengthen the technical justification.\n3) In the current design, four independent Mamba blocks are used to process the four directional sequences. The lack of weight sharing or cross-branch interaction mechanisms raises concerns about potential redundancy and limits the modeling of complementary information across directions.", "was_revised": true, "final_justification": "The authors have addressed my concerns, and I find the paper’s contributions:  being among the first to apply Mamba to MVS, effectively modeling long-range dependencies with linear complexity, and achieving competitive performance with low computational cost, sufficient to meet the acceptance bar.", "rebuttal": "We thank the reviewer for the constructive comments. We provide our feedback as follows.\n\n> **Q1: As shown in Table 4, the \"w/o aggregation\" baseline already achieves strong performance. Moreover, when alternative feature aggregation methods such as VMamba are applied, the model achieves an overall score of 0.291, which is even slightly better than the proposed MVSMamba's score of 0.287. Given that the overall score on the DTU dataset can be highly sensitive to various factors (including post-processing hyperparameters) it is unclear whether the proposed method truly brings a meaningful improvement. The authors are encouraged to provide more quantitative depth estimation metrics to better justify the effectiveness of MVSMamba. The baseline without feature aggregation already performs well, and using VMamba instead of your DM-module gives a slightly better score (0.291 vs 0.287). Since the DTU results can be affected by many factors like post-processing, could you provide more detailed depth evaluation metrics (like RMSE or accuracy) to show that your method truly improves performance?**\n\n**A1:** It's important to clarify that the post-processing stage uses a hyperparameter-consistent dynamic fusion strategy with a confidence threshold of 0.55 for all scans in DTU. We provide detailed depth evaluation metrics (resolution: 832×1152) corresponding to the ablation study in Table 3 and Table 4 of the main paper (MAE is evaluated with a resolution of 512×640 during the validation stage) to show our methods truly improve depth performance.\n\n- The first table shows the effectiveness of each component of MVSMamba in terms of depth metrics, achieving the best MAE and RMSE without adding MLP, and the best depth precision without adding SDM.\n- The second table shows the comparison results of depth metrics with other feature aggregation methods and scanning methods. Our proposed MVSMamba achieved the best performance in all depth metrics.\n\n| Modules | MAE (mm)↓ | RMSE (mm)↓ | Precision 1mm (%)↑ | Precision 2mm (%)↑ | Precision 4mm (%)↑ |\n| :------ | :-------: | :--------: | :----------------: | :----------------: | :----------------: |\n| full    |   9.59    |   27.78    |       66.01        |       78.06        |       84.09        |\n| w/o DM  |   11.29   |   30.72    |       64.16        |       76.46        |       82.58        |\n| w/o SDM |   10.63   |   30.41    |     **66.75**      |     **78.54**      |     **84.34**      |\n| w/o MLP | **8.72**  | **26.04**  |       65.58        |       77.89        |       83.96        |\n\n| Methods         | MAE (mm)↓ | RMSE (mm)↓ | Precision 1mm (%)↑ | Precision 2mm (%)↑ | Precision 4mm (%)↑ |\n| --------------- | :-------: | :--------: | :----------------: | :----------------: | :----------------: |\n| w/o Aggregation |   10.31   |   30.06    |       63.28        |       76.29        |       82.79        |\n| w/ DCN          |   11.73   |   32.83    |       65.01        |       77.11        |       82.88        |\n| w/ FMT          |   17.83   |   45.98    |       64.01        |       75.50        |       80.89        |\n| w/ ET           |   13.24   |   35.34    |       65.52        |       77.33        |       83.08        |\n| w/ VMamba       |   11.58   |   30.83    |       65.63        |       77.57        |       83.39        |\n| w/ EVMamba      |   12.59   |   33.78    |       64.91        |       77.16        |       83.06        |\n| w/ JamMa        |   15.89   |   40.08    |       57.53        |       71.95        |       79.49        |\n| MVSMamba        | **9.59**  | **27.78**  |     **66.01**      |     **78.06**      |     **84.09**      |\n\n> **Q2: The module employs directional feature concatenation (e.g., horizontal-right, horizontal-left, vertical-bottom, vertical-top) to facilitate scanning from the reference view to source views. However, it remains unclear whether this design considers the relative spatial positions between views. A quantitative analysis comparing different concatenation strategies and their impact on performance would strengthen the technical justification. The authors concatenate source features around the reference view in four directions. Do these directions consider the actual positions of the views? Have you tested different ways of concatenation to see which one works best?**\n\n**A2:** \n\n- Our current method does not explicitly consider the actual positions of different camera views, which represents a potential direction for future improvement.\n- As shown in the table below, this study evaluates four feature concatenation scanning methods: source-centered static scanning, reference-centered static scanning, source-centered dynamic scanning, and our proposed reference-centered dynamic scanning. The statistical results show that our proposed reference-centered dynamic scanning method achieves the best performance in both point cloud metrics and depth metrics. This is because the source features learn consistent global feature representations from the reference features, enabling reliable feature matching, as shown in Figure 1 in the supplementary materials.\n\n| Scan Methods               | Overall (mm)↓ | Accuracy (mm)↓ | Completeness (mm)↓ | MAE (mm)↓ |\n| -------------------------- | :-----------: | :------------: | :----------------: | :-------: |\n| source-centered static     |     0.300     |     0.318      |       0.282        |   5.42    |\n| reference-centered static  |     0.294     |   **0.310**    |       0.278        |   5.28    |\n| source-centered dynamic    |     0.296     |     0.312      |       0.280        |   5.39    |\n| reference-centered dynamic |   **0.287**   |     0.314      |     **0.260**      | **5.21**  |\n\n| Scan Methods               | MAE (mm)↓ | RMSE (mm)↓ | Precision 1mm (%)↑ | Precision 2mm (%)↑ | Precision 4mm (%)↑ |\n| -------------------------- | :-------: | :--------: | :----------------: | :----------------: | :----------------: |\n| source-centered static     |   12.11   |   33.09    |       64.07        |       76.90        |       82.97        |\n| reference-centered static  |   9.97    |   27.97    |       63.93        |       76.90        |       83.15        |\n| source-centered dynamic    |   10.21   |   28.67    |       64.54        |       76.99        |       83.22        |\n| reference-centered dynamic | **9.59**  | **27.78**  |     **66.01**      |     **78.06**      |     **84.09**      |\n\n> **Q3: In the current design, four independent Mamba blocks are used to process the four directional sequences. The lack of weight sharing or cross-branch interaction mechanisms raises concerns about potential redundancy and limits the modeling of complementary information across directions. The authors use four independent Mamba blocks for the four directions. Why not share weights between them? Is there any analysis on whether they learn different or similar information?**\n\n**A3:** As the table below shows, ablation experiments determine whether the four Mamba modules share weights. The four independent Mamba modules that do not share weights achieve better performance. Due to Mamba's efficiency, this configuration increases the number of parameters by only 0.1M. This indicates that the four independent Mamba modules help different scanning directions learn different information from the sequence, thereby improving the model's performance.\n\n| Share Weights | Overall (mm)↓ | Accuracy (mm)↓ | Completeness (mm)↓ | MAE (mm)↓ | Params(M)↓ |\n| :------------ | :-----------: | :------------: | :----------------: | :-------: | :--------: |\n| √             |     0.289     |     0.314      |       0.264        |   5.36    |  **1.21**  |\n| ×             |   **0.287**   |   **0.314**    |     **0.260**      | **5.21**  |    1.31    |\n\n| Share Weights | MAE (mm)↓ | RMSE (mm)↓ | Precision 1mm (%)↑ | Precision 2mm (%)↑ | Precision 4mm (%)↑ |\n| ------------- | :-------: | :--------: | :----------------: | :----------------: | :----------------: |\n| √             |   10.67   |   29.74    |       66.00        |       77.88        |       83.76        |\n| ×             | **9.59**  | **27.78**  |     **66.01**      |     **78.06**      |     **84.09**      |"}]

Author rebuttal:

Meta-review: To fully exploit Mamba’s potential in MVS, the paper proposed a Dynamic Mamba module (DM-module) based on a reference-centered
 dynamic scanning strategy, which enables: (1) Efficient intra- and inter-view feature interaction from the reference to source views, (2) Omnidirectional multi-view feature representations, and (3) Multi-scale global feature aggregation.

The major weaknesses lie in 1) incremental contribution by adopting existing Mamba-based work to multi-view stereo; 2) real-world evaluation; 3) comparison with very recent structure-from-motion pipeline such as VGGT. The authors provided rebuttal to address these comments in details, which successfully convince the reviewers. 

Given the consistent 4XBorderline accept ratings from the reviewers, I would like to suggest to accept the paper. Nevertheless, the authors are requested to include all the deatils during the rebuttal period to the revised version.
Decision: Accept (poster)