Test3R: Learning to Reconstruct 3D at Test Time
Authors: Yuheng Yuan
Qiuhong Shen
Shizun Wang
Xingyi Yang
Xinchao Wang
Venue: NeurIPS 2025 poster ()
Keywords: 3D reconstruction
Primary area: applications

Abstract: Dense matching methods like DUSt3R regress pairwise pointmaps for 3D reconstruction. However, the reliance on pairwise prediction and the limited generalization capability inherently restrict the global geometric consistency. In this work, we introduce \textbf{Test3R}, a surprisingly simple test-time learning technique that significantly boosts geometric accuracy. Using image triplets ($I_1,I_2,I_3$), Test3R generates reconstructions from pairs ($I_1,I_2$) and ($I_1,I_3$). The core idea is to optimize the network at test time via a self-supervised objective: maximizing the geometric consistency between these two reconstructions relative to the common image $I_1$. This ensures the model produces cross-pair consistent outputs, regardless of the inputs. Extensive experiments demonstrate that our technique significantly outperforms previous state-of-the-art methods on the 3D reconstruction and multi-view depth estimation tasks. Moreover, it is universally applicable and nearly cost-free, making it easily applied to other models and implemented with minimal test-time training overhead and parameter footprint. Code is available at https://github.com/nopQAQ/Test3R.

Test3R: Learning to Reconstruct 3D at Test Time
Yuheng Yuan'
Qiuhong Shen' Shizun Wang' Xingyi Yangå. Xinchao Wang
National University of Singapore, "The Hong Kong Polytechnic University yuhengyuandu. nus edu, you edu. hk, zinchaodnus edu.
Project page: https: http:::/teestrrnopp htp.teetttttooogi..u..bb.bbebb github.io/
Test3R (Ours)
TVV
M&
DUSt3R
IOyn
Figure I: Given a set of images of a specific scene, Test3R improves the quality of reconstruction by maximizing the consistency between the pointmaps generated from multiple image pairs.
Abstract
Dense matching methods like DUSt3R regress pairwise pointmaps for 3D recon- struction. However, the reliance pairwise prediction and the limited gener- alization capability inherently restrict the global geometric consistency. In this work, introduce Test3R, surprisingly simple test-time learning technique that significantly boosts geometric accuracy. Using image triplets (71, 12, Tz), Test3R generates reconstructions from pairs (7,, T2) and (J1, Tz). The core idea is to op- timize the network at test time via a self-supervised objective: maximizing the geometric consistency between these two reconstructions relative the common image /T. This ensures the model produces cross-pair consistent outputs, regardless of the inputs. Extensive experiments demonstrate that our technique significantly outperforms previous ofte-of-thh-- art methods on the 3D reconstruction and multi- view depth estimation tasks. Moreover,
universally applicable and nearly cost-free, making it easily applied to other models and implemented with min- imal test-time training overhead and parameter footprint. Code available wtwp:hwww.cmmmmo/////ssttt
"Corresponding Author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

I
I Is
I1
I
Figure 2: Inconsistency Study. On the left are two image pairs sharing the same reference view 17 but with different source views I2 and Ig. On the right are the corresponding point maps, with each color indicating the respective image pair.
Introduction
3D reconstruction from multi-view images is a cornerstone task in computer vision. Traditionally, this process has been achieved by assembling classical techniques such as keypoint detection (- 3 and matching 4 [S1, robust camera estimation [4] 6]. Pruucure-from-MotiinnSs
Bundle and dense Multi- Viet Stereo 10 I1]. Although effective, these multi-
Adjustment(BA)
stage methods require significant engineering effort to manage the entire process. This complexity inherently constrains their scalability and efficiency.
Recently, dense matching methods, such as DUS:3R compelling alternatives. At its core, DUSt3R utilizes deep neural network trained predict dense correspondeeces between image pairs in an end-to-end fashion. Specifically, DUSt3R takes two images and, for each, predicts pointaa.. Each pointmap represents the 3D coordinates of every pixel, as projected into common reference view's 's coordinate system. Once pointmaps are generated from multiple views, DUS aligns them by optimizing the registration of these 3D points. This process recovers the camera pose for each view and reconstructs the overall 3D geometry.
and MAST3R 13 have emerged as
Despite its huge success, this pair-wise prediction paradigm is inherently problematic. Under such design, the model considers only two images at a time. Such a constraint leads to several issues.
To investigate this, we compare the pointmaps of image I but with different views I2 and I3. in Figure 2] demonstrates that the predicted pointmaps are imprecise and inconsistent. Firstly, the precision of geometric predictions can suffer because the model is restricted to inferring scene geometry from just one image pair. This is especially true for bhoor-ba cases [14, where small camera movement leads poor triangulation and thus inaccurate geometry. Second, reconstructing an entire scene requires pointmaps from multiple image pairs. Unfortunately, these individual pairwise predictions may not be mutually consistent. For example, the pointmap predicted from (J, Iz. T.) may not align with the prediction from (I1, &2. Iz), as highlighted by the color difference in Figure 2 This local inconsistency further leads to discrepancies in the overall reconstruction. What makes things worse, the model, like many deep learning systems, struggles to generalize to new or diverse scenes. Such limitations directly exacerbate the previously discussed problems of precision and inter-pair consistency. Consequently, even with a final global refinement stage, inaccurate pointmaps lead to persistent errors.
To address these problems, this paper, we present Test3R, a novel yet strikingly simple solution for 3D reconstruction, operating entirely at test time. Its core idea is straightforward: Maximizing the consistency between the reconstructions generated from multiple image pairs. This principle is realized through two basic steps:
Given image triplets (4,, /2, 1a), Test3R first estimates two initial pointmaps with respect to I;: X, from pairs (41, I2) and X, from (4,, Ig).
2.
Test3R optimizes the network, that the two pointmaps are cross-pair consistent, i.c., X1 R X2. Critically, this optimization performed at test time via prompt tuning [1]]
Despite its simplicity, Test3R offers robust solution to all challenges mentioned above. It ensures consistency by aligning local two-view predictions, which resolves inconsistencies. This same mechanism also improves geometric precision: if a pointmap from short-bascline images is imprecise,

---

Test3R pushes it closer to an overall global prediction, which reduces errors. Finally, Test3R adapts to new, unseen scenes, minimizing errors on unfamiliar data.
We evaluated Test3R on the DUSt3R for 3D reconstruction and multi-view depth estimation. Test3R performs exceptionally well across diverse datasets, improving upon vanilla DUSt3R to achieve competitive or state-of-the-an results in both tasks. Surprisingly, for multi-view depth estimation, Test3R even surpasses baselines requiring camera poses and intrinsics, as well as those trained on the same domain. This further validates our model's robustness and efficacy.
The best part is that Test3R is universally applicable and nearly cost-free for these pair-wise methods. This means it can easily be applied to other models sharing a similar pipeline. We validated this by incorporating our design into two categories of models, pairwise methods like MonST3R [T6] and MAST3R [33. and multi-view models like VGGT 17 Experimental results confirmed substantial performance improvements for both models.
The contributions of this work are as follows:
We introduce Test3R, novel yet simple solution to learn the reconstruction test time. It optimizes the model via visual prompts to maximize the cross-pair consistency. It provides a robust solution the challenges of the pairwise prediction paradigm and limited generalization capability.
We conducted comprehensive experiments across several downstream tasks on the DUSt3R, Exper- iment results demonstrate that Test3R not only improves the reconstruction performance compared to vanilla DUSt3R but also outperforms a wide range of baselines.
Our design is universally applicable and nearly cost-free. It can be easily applied to other models and implemented with minimal test-time training overhead and parameter footprint.
2 Related Work
2.1 Multi-view Stereo
Multi-view Stereo(MVS) aims densely reconstruct the geometry of scene from multiple overlap- ping images. Traditionally, all camera parameters are often estimated with SiM [18) as the given input, Existing MVS approaches can generally be classified into three categories: traditional hand- crafted [1 21 global optimization [52 and learning-basee methods [10 [6+22]] Recently,
DUS1R 12] has attracted significant attention as a representative of learning-basee methods. attempts to estimate dense pointmaps from pair of views without any explicit knowledge of the camera parameters. Subsequent tremendous works focus on improving its efficiency [17 30 31].
quality 13 17 32), and broadening its applicability to dynamic reconstruction [6 33- and 3D
perception 36 The majority employ the pairwise prediction strategy introduced by DUSB3 [2]
However, the pair-wise prediction paradigm is inherently problematic. It leads to low precision and mutually inconsistent pointmaps. Furthermore, the limited generalization capability of the model exacerbates these issues. This challenge continues even with the latest models [17 37), which can process multiple images in a single forward pass. While potentially more robust, these newer approaches demand significantly larger resources for training and, importantly, still face challenges in generalizing to unseen environments. To this end, we introduce a novel test-time training technique. This simple design ensures the cross-pairs consistency by aligning local two-view predictions push the pointmaps closer to an overall global prediction, which addresses all challenges mentioned above.
2.2 Test-time Training
The idea of training on unlabeled test data dates back the 1990s 38 called transductive learning. As Vladimir Vapnik [39] famously stated, "Try to get the answer that you really need but not more general one", this principle has been widely applied SVMs 40 41 and recently in large language models (2 Another early line of work is local learning 45 for each test input, "local" model
trained on the nearest neighbors before prediction is made. Recently, Test-time training(TTT) 455 proposes general framework for test-time training with self- learning, which produces a different model for every single test input through the self-supervision task. This strategy allows the model trained on the large-scale datasets to adapt to the target domain at test time. Many other works have followed this framework since then 46.49] Inspired by these studies, we introduce Test3R, novel yet simple technique that extends the test-time training paradigm to the 3D reconstruction
3

---

domain. Our model exploits the cross- cross-pairs consistency as a strong self self-supervised objective to optimize the model parameters at test time, thereby improving the final quality of reconstruction.
2.3 Prompt tuning
Prompt tuning was first proposed as a technique that appends learnable textual prompts to the input sequence, allowing pre-trained language models to adapt downstream tasks without modifying the backbone parameters [50]. In follow-up research, a portion of studies [51 [2 explored strategies for crafting more effective prompt texts, whereas others (53)55] proposed treating prompts as learnable, task-specific continuous embeddings, which are optimized via gradient descent during fine-tuning referred as Prompt Tuning. In recent years, prompt tuning has also received considerable attention in the 2D vision domain. Among these. Visual Prompt Tuning (VPT) [15] has gained significant attention as an efficient approach specifically tailored for vision tasks. It introduces set of Iearnable prompt tokens into the pretrained model and optimizes them using the downstream task's supervision while keeping the backbone frozen. This strategy enables the model to transfer effectively to downstream tasks. In our study, we leverage the efficient fine-tuning capability of VPT to optimize the model ensure the pointmaps are cross-viee vveww consistent. This design makes our model nearly cost-free, requiring minimal test-time training overhead and a small parameter footprint.
3 Preliminary of DUS13R
Given set of images {I& ) of specific scene, DUSt3R [2] achieves high precision 3D reconstruction by predicting pairwise pointmaps of all views and global alignment.
Pairwise prediction. Briefly, DUSt3R takes pair of images, 12, 12 R input and outputs the corresponding pointmaps X1.1, X E @xxHxx which are expressed in the same coordinate frame of f1. In our paper, refer to the viewpoint of 7 as the reference view, while the other is the source view. Therefore, the pointmaps X1.1, X can be denoted as rrefref, xrrereff respectively.
In more detail, these two input images fref, r are first encoded by the same weight-sharing ViT-based model [56] with Ne layers yield two token representations Fref and pere:
Fref = (Fee)er((F"e),
perc = Encoder
After encoding, the network reasons over both of them jointly in the decoder. Each decoder block also attends tokens from the other branch:
Gref Decoder Block ref
(Gref G;T1)
G. = Decoder Block"" GEE)
(3)
where i = 1.... .Na for decoder with Na decoder layers and initialized with encoder tokens Gref Fref and G pare, Finally, in each branch, a separate regression head takes the set of decoder tokens and outputs a pointmap and an associated confidence map:
Xrefsref Cref.ref Head ( G
X xsroreff Crrcreff Head (G .N.).
(5)
Global alignment. After predicting all the pairwise pointmaps, DUS13R introduces a global align- N. ment to handle pointmaps predicted from multiple images. For the given image set {I) DUSt3R first constructs a connectivity graph G(V,E) for selecting pairwise images, where the vertices represent N, images and each edge E E is an image pair. Then, it estimates the depth maps D = } and camera pose π. = - by
arg min D EEE
ΣΣ DD. - -T.
(6)
DEE
where a = {or} } are the scale factors defined on the edges, Pa(TT, X:) means projecting the predicted pointmap X to view v using poses n. to get depth map. The objective function in eq. (6) explicitly constrains the geometry alignment between frame pairs, aiming to preserve cross-view consistency in the depth maps.

---

Methods
Test3R is test-time training technique that adapts DUSt3R [2]
challenging scenes. improves reconstruction by maximizing cross-pair consistency. We begin by analyzing the root cause of inconsistency in Sec. 4.1 In Sec. 4.2 we establish the core problem and define the test-time training objective. Finally, we employ prompt tuning for efficient test-time adaptation in Sec. 4.3
4.1 Cross-pair Inconsistency
DUSt3R 12 aims achieve consistency through global alignment; however, the inaccurate and inconsistent pointmaps lead to persistent errors, significantly compromising the effectiveness of global alignment.
Input Images
Inconsistent pairs
Prompt Tuning
Adapted DUSI3R
ft
Decoder header
Encoder
8y
Prompt
aacl
Shared Weights
Encoder Leyery
Ng
Xrefree
Geometric Consistency Eq 7
Pr
Eg
Encoder
Dacoder & head
Prompt
R
rne
Shared Weights
xrefieet
Encoder Layer,
N1
Encoder
Fg
Prompt
Patch Embed
Pretrained DUS:3R
anc2
Figure 3: Overview of Test3R. The primary goal of Test3R is adapt a pretrained reconstruction model fo to the specific distribution of test scenes fi. It achieves this goal by optimizing set of visual prompts at test time through a sulf-ssupe training objective that maximizes cross-pair consistency between X Xref.ref and X.effref
Therefore, we show a qualitative analysis of the quality of pointmaps on the DTU 57 and ETH3D [58] datasets. Speeifically,, we compare the pointmap for the same reference view but paired with two different source views, and align these two pointmaps to the same coordinate system using Iterative Closest Point (ICP). The result is shown Figure 2 On the left are two image pairs sharing the same reference view but with different source views. On the right are the corresponding pointmaps, with each color indicating the respective image pair.
Observations. These two predicted pointmaps of the reference view exhibit inconsistencies. as highlighted by the presence of large regions with inconsistent colors in 3D space. Ideally, if these pointmaps consistent, they should be accurate enough to align perfectly in 3D space, resulting in a single, unified color (either blue or red). This result indicates that DUSt3R may produce different pointmaps for the same reference view when paired with different source views.
In our view, this phenomenon stems from the problematic pair-wise prediction paradigm. First, since only two views are provided as input at each prediction step, the scene geometry is estimated solely based on visual correspondences between single image pair. Therefore, the model produces inaccurate pointmaps. Second, all predicted pointmaps are mutually inconsistent individual pairs. For different image pairs, their visual correspondences are also different. As result, DUSt3R may produce inconsistent pointmaps for the same reference view when paired with different source views due to the different correspondences. This issue significantly hinders the effectiveness of subsequent global alignment and further leads to discrepancies in the overall reconstruction. What's worse, the limited generalization capability of DUSt3R further exacerbates the above issues of low precision and cross-pair inconsistency.
5

---

4.2 Triplet Objective Made Consistent
The inconsistencies observed above highlight a core limitation of the pairwise prediction paradigm. Specifically, DUSt3R may produce different pointmaps for the same reference view when paired with different source views. This motivates a simple but effective idea: enforce triplet consistency across these pointmaps directly at test time, as shown in Figure 3
Definition, We first describe the definition of test-time training on the 3D reconstruction task, where only images ( from the test scene available. During training time training phase, N, labeled collected from various scenes are given, where I I, and X X. are images
Ti samples { J - and the corresponding pointmaps derived from the ground-truth depth D, E D., Furthermore, we denote DUSI3R (2). parameterized by 0, as the model trained to learn the reconstruction function f. I. - X.. Subsequently, during test time training phase, only unlabeled images (1; from test scene are available, where T E 4. Our goal is to optimize the model f& to the specific scene ft I - Rr at test time. This is achieved by minimizing the self-supervised training objective E.
Specifically, our core training objective is maximize the geometric consistency by aligning the pointmaps of the reference view when paired with different source views. For set images {T} )= from the specific scene, we consider triplet consisting of one reference view and two different source views, denoted as (rre,, Torel, ]erc2). Subsequently, Test3R forms two reference-soure view pairs (1ref, I I and (1ref, _ from this triplets. These reference-souree view pairs are then fed into the Test3R independently to predict pointmaps of reference views under different source view conditions in the same coordinate frame of pre/, denoted as Xx and xxef,ref, Finally, we construct the training objective by aligning these two inconsistent pointmaps, formulated as:
Xrerree -xte
With this objective, we can collectively compose triplets from large number of views of an unseen 3D scene at test time. It guides the model to successfully resolve the limitations mentioned in Section 4.1 For inconsistencies, ensures consistency by aligning the local two-view predictions. Mcanwhile, also pushes the predicted pointmap closer an overall global prediction to mitigate the inaccuracy. Moreover, by optimizing for the specific scene at test time, it enables the model to adapt to the distribution of that scene.
4.3 Visual Prompt Tuning for Test Time Training
After the self-supervised training objective is defined, effectively modulating the model during test- time training for specific scenes remains a non-trivial challenge. During the test-time training phase, only relies on unsupervised training objectives. However, these objectives are often noisy and unreliable, which makes the model prone to overfitting and may lead to training collapse, especially when only a limited number images are available for the current scene. Fortunately, similar issues has been partially explored in the 2D vision community. In these works, visual prompt tuning [15) has demonstrated strong effectiveness domain adaptation in 2D classification tasks [59]. It utilizes a set of learnable continuous parameters to learn the specific knowledge while retaining the knowledge learned from large-scal scale pretraining. Motivated by this, we explore the use of visual prompts as a carrier to learn the geometric consistency for specific scenes.
of learnable prompts into the encoder of DUSt3R 12 Consider an encoder of DUSt3R with Ne standard Vision Transformer( ViT) [56) layers, an input image is first divided into fixed-sized patches and then embedded into d-dimensional tokens Eg- = (e) E 18 " e N, 1 < k < Nr}. where N, is the length of image patch tokens. Subsequently, to optimize the model, we introduce a set of learnable prompt tokens
Specifically, we incorporate
N. into each Transformer layer. For i th transformer layer, the prompt tokens are denoted as P = {pf_ E Plk E N, N, S k < Npp. where N, is the length of prompt tokens. Therefore, the encoder layer augmented by visual prompts is formulated as:
L. Ejj Ej] = ((
(8)
where P,-1 and E.-1 are learnable prompt tokens and image patch tokens at i 1-th Transformer layer.

---

Reference
GT
CUT3R
DUS:3R
Test3R
(a) Office-09
(b)
Figure 4: Qualitative Comparison on 3D Reconstruction.
Test-time training. We only fine-tune the parameters of the prompts, while all other parameters fixed. This strategy enables our model to maximize geometric consistency by optimizing the prompts test time while retaining the reconstruction knowledge acquired from large-scale datasets training within the unchanged backbone.
Experiment
We evaluate our method across a range of 3D tasks, including 3D Reconstruction Section 5.1 and Multi-view Depth( Section Moreover, we discuss the generalization of Test3R and the prompt Additional experiments and detailed information are provided in the appendix.
design( Section 5.3
Baselines. Our primary baseline is DUSt3R [2], which serves the backbone of technique in the experiment. Subsequently, we select different baselines for the specific tasks to comprehensively evaluate the performance of our proposed method. For the 3D reconstruction task, which is the primary focus of the majority of 3R-series models, we compared our method with current mainstream approaches evaluate its effectiveness. It includes MAST3R 13] MonST3R [16), CUT3R 35) and Spann3R 331 All of these models are follow-up works building on the foundation established by DUS(3R [12] Furthermore, for the multi-view depth estimation task, we not only compare our model with baselines 660 6 that do not require camera parameters but also evaluate our model against 63 64 that rely on camera parameters trained on datasets from the same distribution to demonstrate the effectiveness of our technique.
methods (9
5.1 3D Reconstruction
We utilize two scene-level datasets, 7Scenes (65 and NRGBD 66 datasets. We follow the exper- iment setting on the CUT3R (35), and employ several commonly used metrics: Accuracy (Acc), Completion (Comp), and Normal Consistency (NC) metrics. Each scene has only to 5 views available for the 7Scenes (65] dataset and 2 to 4 views for NRGBD [66] dataset. This is highly challenging experimental setup, as the overlap between images each scene is minimal, demanding strong scene reconstruction capability.
Quantitative Results. The quantitative evaluation shown in Table Compared to vanilla DUSt3 [2]. our model demonstrates superior performance, outperforming DUSt3R on the majority of evaluation metrics, particularly terms of mean accuracy and completion. Moreover, our approach achieves comparable even superior results compared to mainstream methods. Only CUT3R [35] and MAST3R [K] outperform our approach on several metrics. This demonstrates the effectiveness of our test-time training strategy.
7

---

Qualitative Results. The qualitative results are shown in Figure We compare our method with CUT3R [35 and DUSt3R [[2] on the Office and Kitchen scenes from the 7Scenes [65] and NRGBD (66] datasets, respectively. We observe that DUSt3R incorrectly regresses the positions of scene views, leading to errors in the final scene reconstruction. In contrast, our model achieves more reliable scene reconstructions. This improvement is particularly evident in the statue in the Office scene and the wall in the Kitchen scene, For these two objects, the reconstruction results from DUS:3R are drastically different from the ground truth. Compared to CUT3R [35], the current state- of-the-art 3D reconstruction, we achieve better reconstruction results. Specifically, we effectively avoid the generation of outliers, resulting in more accurate pointmaps. Details can be seen in the red bounding boxes as shown Figure
Table 1: 3D reconstruction comparison on 7Scenes and NRGBD datasets.
7Seenes
NRGBD
Acci
Compl
NCT
Acc.
Compl
NCT
Method
Mean
Med.
Mcun
Med.
Mean
Mod.
Mean
Med.
Mean
Med.
Mean
Med.
MAST3R
13
0.189
0.109
0.211
0.110
0.687
0.766
0.085
0.033
0.063
0.028
0.794
0.928
MonST3R
16
0.240
0,180
0.268
0.167
0.672
0.758
0.272
0.114
0.287
0.110
0.758
0.843
Spann3R
0.650
CUT3R
335
0.126
0.047
0.154
0.031
0.727
0.834
0.099
0.031
0.076
0.026
0.837
0.971
DUS(R
12
0.146
0.078
0.181
0.067
0.736
0.839
0.144
0.019
0.154
0.018
0.871
0.982
Test3R(Ours)
0.105
0.051
0.136
0.035
0.746
0.855
0.083
0.021
0.079
0.019
0.870
0.983
Table 2: Multi-view depth evaluation. (Parentheses) denote training on data from the same domain.
GT
ETH3D
AVG
Method
Align
Pose
Range
Intrinsics
rel
4
rel
COLMAP 1111
X
X
0.7
96.5
16.4
55.1
8.6
75.8
COLMAP Dense 11
X
X
20.8
69.3
89.8
23.2
55.3
46.3
MVSNet
V
(1.8)
(86.0)
18.6
58.7
Vis NVLSNN
65
V
X (1.8)
(87.4)
10.8
43.3
6.3
65.4
MVS2D ScunNet
[64
V
X
17.2
9.8
27.4
4.8
22.3
7.3
MVS2D 64
V
(3.6)
(64.2)
DeMoN [i
X
X
23.7
11.5
19.0
16.2
21.4
13.9
DeD KITTI
[6]
X
24.6
8.2
30.1
9.4
27.4
8.8
DeepV2DD ScanNet
6
X
X
9.2
27.4
18.7
28.7
14.0
28.1
MVS2D ScanNet
[6d
X
X 5.0
$7.9
30.7
14.4
17.9
36.2
Robust MVD Baseline [2]
2.7
82.0
42.6
5.9
62.3
DeMoN 60
X
||:||
21.8
16.6
17.4
15.4
19.6
16.0
DeeDVDD KITTI
X
med
24.8
8.1
27.1
10.1
26.0
9.1
Vec ScanNet 61
X
med
7.7
33.0
11.8
29.3
9.8
62.3
DUS(3R
73.0
71.5
Turr)((Our))
med
2.0
84.1
3.2
74.0
2.6
79.1
5.2 Multi-view Depth
Following RobustMVD 662. performances are measured on the object-centric dataset DTU |57 and scene-centric dataset ETH3D [58) To evaluate the depth map, report the Absolute Relative Error (rel) and the Inlier Ratio (r) at a threshold of 3% on each test set and the averages across all test sets.
Quantitative Results. The quantitative evaluation is shown in Table On the DTU dataset, our model significantly improves upon the performance of vanilla DUSBR, reducing the Absolute Relative Error by 1.3 and increasing the Inlier Ratio by 14.2. Similarly, on the ETH3D dataset, our model also demonstrates comparable improvements, achieving state-of-the-art performance on this challenging benchmark as well. Notably, our model surpasses the majority of methods that rely on camera poses and intrinsic parameters, and the models trained on the dataset from the same domain. This indicates that our approach effeectivlly captures scene-specific global information and enables the adaptation of the distribution of test scenes, thereby significantly improving the quality of the depth maps.
Qualitative Results. The qualitative result is shown in Figure 5 We present the depth map on the key view, following RobustMVD [62]. We observe that Test3R effectively improves the accuracy of depth estimation compared to DUSt3R and RobustMVD [2] with camera parameters. Specifically, Test3R captures more fine- grained details, including the computer chassis and table. Additionally, on the white- backgroudd DTU dataset, Test3R effectively understands scene context, allowing it to accurately estimate the depth of background regions.

---

Reference
RkvT(K+RT)
DUS:3R
Test3R
(a) ETH3D
(b) DTU
Figure 5: Qualitative Comparison on Multi-view Depth.
5.3 Ablation Study and Analysis
5.3.1
Framework Generalization.
To demonstrate the generalization ability of our proposed technique, we applied Test3R to MAST3R [13] and MonST3R [16]. and evaluated the performances on the 7Scenes 665 dataset. As shown in Table 3 Test3R effectively improves the performance of MAST3R and MonST3R on 3D reconstruction task. This demonstrates the generalization ability of our technique, which can be applied to other models sharing similar pipeline.
5.3.2 Ablation on Visual Prompt.
We introduce model variant, Test3R-S, and conduct an ablation study to evaluate the impact of visual prompts. For Test3R-S, the prompts are only inserted into the first Transformer layer, accompany the image tokens through the encoding process, and are then discarded.
Table 3: Generalization Study.
Table 4: Ablation study Visual Prompt.
75cenes
Phumpi Leng
Acei
Compl
NC1
Method
Mod.
Mod.
Med.
Naiers
MAST3R
0.189
0.109
0,211
0.110
0.687
0.766
Asc.
Cure.
Comp)
Assj
Campi
Accj
Corrpi
MAST3RIW. Tes3R)
0.179
0.108
0.177
0.059
0.702
0.788
MonST3R
16
0.240
0.180
0.672
0.758
Tea3R-S
0.133
0.142
0.125
0.59
0.100
0.158
0.115
0.16
Tes33R)
0.218
0.167
0.251
0.160
0.687
0.775
The result is shown Table 4 Both Test3R-S and Test3R effectively improve model performance. compared to vanilla DUS(3R, For prompt length, we observe that when the number of prompts is small, increasing the prompt length can enhance the ability of Test3R improve reconstruction quality. However, as the prompt length increases, the number of trainable parameters also grows, making more challenging converge within the same number of iterations, thereby reducing their overall effectiveness. For prompt insertion depth, we observe that Test3R, which uses distinct prompts at each layer, demonstrates superior performance. This is because the feature distributions vary across each layer of the encoder of DUSt3R, making layer-specific prompts more effective for fine-tuning. However, as number of prompt parameters increases, Test3R becomes more susceptible optimization challenges compared to Test3R-S. leading to faster performance decline.

---

Conclusion
In this paper, we present TestRR, novel yet strikingly simple solution that learns to reconstruct at test time. It maximizes the cross-pair consistency via optimizing set of visual prompts at test time. This design successfully mitigates the reconstruction quality degradation caused by the pairwise predictions paradigm and limited generalization capability. Extensive experiments show that our simple design not only effectively improves model performance but also achieves state-of- ootte-ffthe-art performance across various tasks. Moreover, our technique is universally applicable and nearly cost-free, which can be widely applied to different models and implemented with minimal test-time training overhead and parameter footprint.
Acknowledgement
This project is supported by the National Research Foundation, Singapore, under its Medium Sized Center for Advanced Robotics Technology Innovation.
References
Mihai Dusmanu, Ignacio Rocco, Tomas Pajdla, Marc Pollefeys, Josef Sivic, Akihiko Torii, and Torsten Sattler. D2-net: trainable enn for joint description and detection of local features. In Proceedings of the ieee/cvf conference on computer vision and pattern recognition, pages 8092-8101, 2019.
[2] Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superpoint: Self-supervised interest point detection and description. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 224-236, 2018.
[3] David G Lowe. Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60:91-110, 2004. 2004.
[4] Eric Brachmann and Carsten Rother. Neural-guiee ransac: Learning where to sample model hypotheses. 2019,
In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages
Philipp Lindenberger, Paul-Edouard Sarlin, and Mare Pollefeys. Lightglue: Local feature matching light speed. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 17627-17638, 2023.
[6] Chen Zhao, Yixiao Ge, Feng Zhu, Rui Zhao, Hongsheng Li, and Mathicu Salzmann. Progressive correspondence pruning by consensus learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6464-6473, 2021.
[7] David Crandall, Andrew Owens, Noah Snavely, and Daniel Huttenlocher. Sfm with mrfs: Discrete- continuous optimization for large-scale structure from motion, IEEE transactions on pattern analysis and machine intelligence, 35(12):2841 2853, 2012.
[8] Philipp Lindenberger, from-motion with featuremetric refinement. In Proceedings of the IEEF/CVF international conference on computer vision, pages 5987-5997, 2021.
Sarlin, Viktor Larsson, and Mare Pollefeys. Pixel-perfect structure-
[9] Johannes L Schonberger and Jan-Michael Frahm. cemutmure-roommmmttion revisited. In Proceedings of the IEEE conference computer vision and pattern recognition, pages 4 4104-4113, 2016.
[10] Xiaodong Gu, Zhiwen Fan, Siyu Zhu, Zuozhuo Dai, Feitong Tan, and Ping Tan. Cascade cost volume for high-resolution multi-view stereo and stereo matching. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2495-2504, 2020.
[11] Johannes L. Schönberger, Enliang Zheng, Jan-Michacl Frahm, and Marc Pollefeys. Pixelwise view selection for unstructured multi-view stereo. In Computer Vision-ECCC ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part III 14, pages 501-518. Springer, 2016.
[12] Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. Dust3r: Geometric 3d vision made easy. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20697-20789 2024.
10

---

[13] Vincent Leroy, Yohann Cabon, and Jéröme Revaud. Grounding image matching in 3d with mast3r. In European Conference on Computer Vision, pages 71-91. Springer, 2024.
[14] Masatoshi Okutomi and Takeo Kanade. A multiple-baseee baseline stereo. IEEE Transactions on pattern analysis and machine intelligence, 15(4)33533633 1993.
[15] Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie. Bharath Hariharan, and Ser- Nam Lim. Visual prompt luning. European conference on computer vision, pages 709-727. Springer, 2022.
[16] Junyi Zhang, Charles Herrmann, Junhwa Hur, Varun Jampani, Trevor Darrell, Forrester Cole, Deqing Sun, and Ming-Hsuan Yang. Monst3r: A simple approach for estimating geometry in the presence of motion. arXiv preprint 14.00224, 2024.
[17] Jianyuan Wang, Minghao Chen, Nikita Karaev, Andrea Vodaldi, Christian Rupprecht, and David Novotny, Vggt: Visual geometry grounded transformer. arXiv preprint arXiv:2503.1163 2025.
[18] Shimon Ullman. The interpretation of structure from motion. Proceedings of the Royal Society of London. Series B. Biological Sciences, 203(1 153):405-4444, 1979.
[19] Yasutaka Furukawa, Carlos Hernández, al. Multi-view stereo: A tutorial. Foundations and trends@ in Computer Graphics and Vision, 9(1-2)11 148, 2015.
[20] Silvano Galliani, Katrin Lasinger, and Konrad Schindler. Massively parallel multiview stereopsis by surface normal diffusion. In Proceedings of the IEEE international conference on computer vision, pages 37 2015.
[21] Yuesong Wang. Zhaojie Zeng, Tao Guan, Wei Yang. Zhuo Chen, Wenkai Liu, Luoyuan Xu, and Yawei Luo. Adaptive patch deformation for resilietss-resillentt view stereo. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pages 1621-1630, 2023.
[22] Qiancheng Fu, Qingshan Xu, Yew Soon Ong, and Wenbing Tao. Geo-neus: Geomelry-consistent neural implicit surfaces learning for multi-view reconstruction. Advances in Neural Information Processing Systems, 35:3403-3416, 2022.
[23] Michael Niemeyer, Lars Mescheder, Michael Occhsle, and Andreas Geiger. Differentiable volumetric rendering: leamming implicit 3d representations without 3d supervision. In Proceedings of the IFFF/CVF conference on computer vision and pattern recognition, pages 3504-3515, 2020.
[24] Yi Wei, Shaohui Liu, Yongming Rao, Wang Zhao, Jiwen Lu, and Jie Zhou. Nerfingmvs: Guided optimization of neural radiance fields for indoor multi-view stereo. In Proceedings of the IEEE/CVF international conference computer vision, pages 5610 10100-11112000 2021.
[25] Lior Yariv, Yoni Kasten, Dror Moran, Meirav Galun, Matan Atzmon, Basri Ronen, and Yaron Lipman. Multiview neural surface reconstruction by disentangling geometry and appearance. Advances Neural Information Procexsing Systems, 33:2492-2502. 2020.
[26] Zeyu Ma. Zachary Teed, and Jia Deng. Multiview stereo with cascaded epipolar raft. European Conference on Computer Vision, pages 734-750. Springer, 2022.
[27] Rui Peng, Rongjie Wang, Zhenyu Wang, Yawen Lai, and Ronggang Wang. Rethinking depth estimation for multi-view stereo: A unified representation. In Proceedings of the IEEE/CVF conference on computer vision and puttern recognition, pages 8645-8654, 2022.
[28] Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan. Mvsnet: Depth inference for unstructured multi-view stereo. In Proceedings of the Furopean conference computer vision (ECCV), pages 767-783, 2018.
[29] Zhe Zhang, Rui Peng, Yuxi Hu, and Ronggang Wang. Geomvsnet: Learning multi-view stereo with geometry perception. In Proceedings of the IEEE/CVE conference on computer vision and pattern recognition, pages 21508 21518, 2023.
[30] Yuzheng Liu, Siyan Dong, Shuzhe Wang, Yingda Yin, Yanchao Yang, Qingnan Fan, and Baoquan Chen. Slam3r: Real-time dense scene reconstruction from monocular rgb videos. arXiv preprint arXiv:2412.0980 094411, 2024.
[31] Hengyi Wang and Lourdes Agapito. 3d reconstruction with spatial memory. arXiv preprint arXiv:2408. 16061, 2024.
11

---

[32] Wonbong Jang. Philippe Weinzaepfel, Vincent Leroy, Lourdes Agapito, and Jerome Revaud. Pow3r: Em- powering unconstrained 3d reconstruction with camera and scene priors. arXiv preprint arXiv:2503.13316 2025.
[33] Jiahao Lu, Tianyu Huang, Peng Li, Zhiyang Dou, Cheng Lin, Zhiming Cui, Zhen Dong, Sai-Kit Yeung. Wenping Wang, and Yuan Liu, Align3r: Aligned monocular depth estimation for dynamic videos. arXiv preprint arXiv:2412.0307, 2024.
[34] Xingyu Chen, Yue Chen, Yuliang Xiu, Andreas Geiger, and Anpei Chen. Easi3r: Estimating disentangled motion from dust3r without training, arXiv preprint (tt::2503.2499/, 2025.
[35] Qianqian Wang, Yifei Zhang, Aleksander Holynski. Alexei A Efros, and Angjoo Kanazawa, Continuous 3d perception model with persistent state. arXiv preprint arXiv:2501. 2501. 2025.
[36] Jie Hu, Shizun Wang. and Xinchao Wang. Pe3r: Perception-efficient 3d reconstruction. arXiv preprint arXiv:2503.07507, 2025.
[37] Jianing Yang. Alexander Sax. Kevin Liang. Mikael Henaff, Hao Tang. Ang Cao, Joyce Chai, Franziska Meier, and Mat Feiszli. Fast3r: Towards 3d reconstruction of 1000+ images in one forward pass. arXiv preprint ar Xiv:2501.13328, 2025.
[38] Alex Gammerman, Volodya Vovk, and Vladimir Vapnik. Learning by transduction. arXiv preprint 130 2013.
[39] Vladimir Vapaik. Estimation of dependences based on empirical data. Springer Science & Business Media, 2006.
[40] Ronan Collobert, Fabian Sinz, Jason Weston, Léon Bottou, and Thorsten Joachims, Large scale transductive svms. Journal Machine Learning Research, 2006.
[41] Thorsten Joachims. Learning to classify text using support vector machines, volume 668. Springer Science & Business Media, 2002.
[42] Moritz Hardt and Yu Sun. Test-time training on nearest neighbors for large language models. arXiv preprint arXiv:2305. 184666 2023.
[43] Lćon Bottou and Vladimir Vapnik. Local leamingg algorithms. Neural computation, 4(6):888-900 1992.
[44] Hao Zhang, Alexander C Berg, Michael Maire, and Jitendra Malik. Svm-knn: Discriminative nearest neighbor classification for visual category recognition. 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 06). volume 2. pages 2126-22222 2136. IEEE, 2006.
[45] Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexci Efros, and Moritz Hardt. Test-time training with self-supervision for generalization under distribution shifts. In International conference on machine learning, pages
PMLR, 2020.
[46] Nicklas Hansen, Rishabh Jangir, Yu Sun, Guillem Alenyà, Pieter Abheel, Alexei A Efros, Lerrel Pinto, and Xiaolong Wang. Self-supervised policy adaptation during deployment. arXiv preprint arXiv:2007.04309 2020.
[47] Yu Sun, Wyatt Ubellacker, Wen-Loong Ma, Xiang Zhang, Changhao Wang. Noel Csomay-Shankk Shanklin, Masayoshi Tomizuka, Koushil Sreenath, and Aaron D Ames. Online lcarning of unknown dynamics for model-based controllers in legged locomotion. JEEE Robotics and Automation Letters, 6(4):8422 8449, 2021.
[48] Yuejiang Liu, Parth Kothari, Bastien Van Delft, Baptiste Bellot-Gurlet, Taylor Mordan, and Alexandre Alahi. Tu++: When does self-supervised test-time training fail or thrive? Advances in Neural Information Processing Systems, 14-1188-21120 2021.
[49] Longhui Yuan, Binhui Xie, and Shuang Li. Robust test time adaptation in dynamic scenarios. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15522- 15932, 2023.
[50] Ben Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, A Neelakantan, P Shyam, G Sastry, A Askell, S Agarwal, et al. Language models are few-shot learners. arXiv preprint 2rXiv:2005.14166 1:3, 2020.
[51] Zhengbao Jiang, Frank F Xu, Jun Araki, and Graham Neubig. How can we know what language models know? Transactions of the Association for Computational Linguistics, 8:423 438, 2020.
12

---

[52] Taylor Shin, Yasaman Razeghi, Robert L Logan IV. Eric Wallace, and Sameer Singh. Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010. 15980. 2020.
[53] Brian Lester, Rami Al-Rfou, and Noah Constant. The power of scale for parameter-efficient prompt tuning. arXiv preprint arXiv:2104.08691 2021.
[54] Xiang Lisa Li and Percy Liang. Prefix-tuning: Optimizing continuous prompts for generation. arXiv preprint orXiv:2101.00 00190, 2021.
[55] Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Lam Tam, Zhengxiao Du, Zhilin Yang. and Jie Tang. P-tuning v2: Prompt luning can comparable fine-tuning universally across scales and tasks. arXiv preprint arXiv:2110.07602 2021.
[56] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai Thomas Unterthiner. Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010. 11929, 2020.
[57] Henrik Aanæs, Rasmus Ramsbøl Jensen, George Vogiatzis. Engin Tola, and Anders Bjorholm Dahl. Large-scale data for multiple-view stereopsis. International Journal of Computer Vision, 120:153-166 2016.
[58] Thomas Schops, Johannes L Schonberger, Silvano Galliani, Torsten Sattler, Konrad Schindler. Marc Pollefeys, and Andreas Geiger. A multi-view sterco benchmark with high-resolution images and multi- camera vidcos. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3260-3269, 2017.
[59] Yunhe Gao, Xingjian Shi, Yi Zhu, Hao Wang, Zhiqiang Tang, Xiong Zhou, Mu Li, and Dimitris N Metaxas. Visual prompt tuning for test-time domain adaptation. arXiv preprint 10.0042,, 2022.
[60] Benjamin Ummenhofer, Huizhong Zhou, Jonas Uhrig, Nikolaus Mayer, Eddy Ilg, Alexey Dosovitskiy, and Thomas Brox. Demon: Depth and motion network for learning monocular stereo. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5038-5047, 2017.
[61] Zachary Teed and Jia Deng. Deepv2d: Vidco to depth with diffcrentiable structure from motion. arXiv preprint Xrvv / 012..10225 2018.
[62] Philipp Schröppel. Jan Bechtold, Artemij Amiranashvili, and Thomas Brox. A benchmark and baseline for robust multi-view depth estimation. In 2022 International Conference on 3D Vision (3DV), pages 637-645. IEFF, 2022.
[63] Jingyang Zhang, Shiwei Li, Zixin Luo, Tian Fang, and Yao Yao. Vis-mvsnee Visibility multi-view stereo network. International Journal of Computer Vision, 131(1):199225 214, 2023.
[64] Zhenpei Yang, Zhile Ren, Qi Shan, and Qixing Huang. Mvs2d: Efficient multi-view stereo via attention- driven 2d convolutions. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pages 8574-8584, 2022.
[65] Jamie Shotton, Ben Glocker, Christopher Zach, Shahram Izadi, Antonio Criminisi, and Andrew Fitzgibbon. Scene coordinate regression forests for camera relocalization in rgb-d images. In Proceedings of the IEEF conference on computer vision and pattern recognition, pages 2930-2937, 2013.
[66] Dejan Azinović, Ricardo rgb-d surface reconstruction. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6290-6301, 2022.
Dan B Goldman, Matthias Nicßner, and Justus Thics. Neural
[67] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv: 1412.690, 2014.
[68] Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang. Wang, Weizhu Chen, et al. Lora: Low-rank adaptation of large language models. ICLR, 1(2)3, 2022.
[69] Daniel Butler, Jonas Wulff, Garrett B Stanley, and Michael J Black. A naturalistic open source movic for optical flow evaluation. In European conference on computer vision, pages 61 l 1-625. Springer, 2012.
13

---

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: Test3R significantly improves the performance of DUSt3R and can achieve performance comparable to current baselines, as demonstrated in Table [ and Table 2
Moreover, it can also be applied other models Table
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made 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 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 not attained by the paper.
2. Limitations
Question:
Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification:
We discuss the limitations. Please see in appendix.
Guidelines:
The answer NA means that the paper has 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 in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (c.g.. independence assumptions, noiseless settings, model sell-specfiiatiinn asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.
The authors should reflect 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 low or images are taken in low lighting. Or speech-to-tex, system might not be used reliably to provide closed captions for online lectures because it fails 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 faimesss.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't 't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community, Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
14

---

Answer:
[NA]
Justification:
This paper does not involve theoretical result.
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 or referenced in the statement of any theorems.
The proof's can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch provide intuition.
Inversely, any informal proof provided in the core of the paper should 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
Does the paper fully disclose all the information needed to reproduce the main ex- perimental results the paper to the extent that affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided not)?
Question:
Answer: [Yes]
The paper shows the results needed and ablation study in Section All of the results are reproducible.
Justification:
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, descríbing the architecture fully might suffice, or if the contribution is specific model and empirical evaluation, it may be necessary to either make possible for others to replicate the model with the same dataset, provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g.. g., in the case of a 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 submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) the contribution is primarily a new algorithm, the paper should make clear how reproduce that algorithm. (b) If the contribution is primarily new model architecture, the paper should describe the architecture clearly and fully.
(c) the contribution is a new model (e-g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should possible for other researchers to have some path to reproducing verifying the results.
5. Open access to data and code
15

---

Does the paper provide open access to the data and code, with sufficient instruc- tions faithfully reproduce the main experimental results, as described in supplemental material?
Question:
Answer: [No]
Justification:
The code will open-sourced upon acceptance.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurlPS code and data submission guidelines (https://nips.cr cc/
for more details.
While we encourage the release of code and data, we understand that this might not be possible, "No" an acceptable answer. Papers cannot be rejected simply for 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 NeurlPS code and data submission guidelines (https
/ prijps.cc/pbbliccguu.eee CoodeSumeeieeeeee ssi iessonPolll molyicyy for more details.
The authors should provide instructions on data access and preparation, including how to 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 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 detailed experiment settings are listed in Section 5 and appendix.
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core 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, as supplemental material.
7. Experiment statistical significance
Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Question:
Answer: [NA]
Justification: The experiment metries calculated several large-scale datasets. We follow the widely adopted benchmarks and list the essential detailed quantitative results in Section 5
None of them report error bars.
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, or statistical significance tests, least for the experiments that support the 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, or overall with given experimental conditions).
16

---

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).
It should be clear whether the error bar is the standard deviation or the standard error of the mean.
is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma enror bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are range (e.g. negative enror rates).
If error bars reported in tables plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables 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 execution) needed reproduce the experiments?
Question:
Answer: [Yes]
Justification: The paper provides sufficient information on the computer resources ap- pendix.
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 in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).
9. Code of ethics
Question: Does the research conducted the paper conform, in every respect, with the NeurlPS Code of Ethics https: htttp:::///eurppee httpr//s...pppple/ct//cscssieeaa
Answer: [Yes]
Justification:
The research conducted in the paper conforms the NeurIPS 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 sure to preserve anonymity (e.g.. if there is special consid- eration 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: The paper discusses potential positive societal impacts and negative societal impacts.
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.
17

---

Examples of negative societal impacts include potential malicious unintended uses (e.g., disinformation, generating fake profiles, surveillance), faireess considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
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 is being used as intended but gives incorrect results, and harms following from (intentional unintentional) misuse of the technology.
If there are 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 to monitor how system learns from feedback over time, improving the efficiency and accessibility of ML).
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:
This paper does not pose such risk.
Guidelines:
The answer NA means that the paper poses no 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 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. Licenses 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: Proper credit given the creators or original owners of assets used in the paper, and the license and terms of use are explicitly mentioned and respected.
Guidelines:
The answer NA means that the paper does use 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 (e.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.
18

---

If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswi thcode. has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
batasets.papersstt
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. If this information is available online, the authors encouraged to reach out the asset's s creators.
13. New assets
Question: Are assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [NA]
Justification: This paper does not release new assets.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset used.
At submission time, remember to anonymize your assets (if applicable). You can either create anonymized URL or include an anonymized zip file.
14. Crowdsourcing 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 as details about compensation (if any)?
Answer: [NA]
Justification: This paper does not involve crowdsourcing and nor research with human subjects.
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 much detail as possible should be included the main paper.
According to the NeurlPS Code of Ethics, workers involved in data collection, curation, 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 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: Not related.
Guidelines:
The answer NA means that the paper does 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.
19

---

We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors adhere the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.
16. Declaration of LLM usage
Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, formatting purposes and does not impact the core methodology. scientific rigorousness, or originality of the research, declaration is not required.
Question:
Answer: [NA]
Justification: The core method development this research does involve LLMs.
Guidelines:
The answer NA means that the method development in this research does not involve LLMs as any important, original, or non-standard components.
Please refer to our LLM policy bettts://neurre /0tps//eee.o0oeeeeeee/22//////// for what should or should be described.
20


Reviews:
[{"review_id": "EfMswbe8F6", "reviewer": "Reviewer_MVdH", "summary": "This paper introduces Test3R, a test-time learning technique designed to improve the geometric consistency of pairwise dense matching methods like DUSt3R for 3D reconstruction. The authors identify that methods based on pairwise predictions can produce pointmaps that are imprecise and mutually inconsistent when a reference image is paired with different source views. Test3R addresses this by enforcing geometric consistency at test time. For a given image triplet \n(I1​,I2​,I3​), the method generates two reconstructions relative to the common image I1​ (from pairs (I1​,I2​) and (I1​,I3​)) and then optimizes the network via a self-supervised loss to maximize the geometric alignment between them. This optimization is performed efficiently using prompt tuning. Experiments show that this approach significantly improves the performance of DUSt3R on 3D reconstruction and multi-view depth estimation tasks, achieving state-of-the-art results.", "questions": "1. Could you provide a more detailed analysis of the test-time computational overhead? For example, for a typical scene in the 7Scenes or DTU datasets, how many optimization iterations are performed, and what is the total processing time compared to a single inference pass of the original DUSt3R? This is critical for understanding the practical viability of the method.\n\n2. How are the image triplets (I1​,I2​,I3​) selected from the test set? Is the process random, or is there a specific strategy involved? How does the performance of Test3R vary with different numbers of triplets or different selection strategies (e.g., prioritizing wider baselines)?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The core idea of Test3R is simple yet effective. Enforcing consistency between two different pairwise predictions that share a common view is an effective self-supervision signal that directly targets the problem with pairwise reconstruction methods.\n2.  The method demonstrates significant performance improvements over its baseline (DUSt3R) and achieves state-of-the-art or competitive results on multiple challenging benchmarks for both 3D reconstruction and multi-view depth estimation.\n3. The use of visual prompt tuning for the test-time optimization makes sense. It allows the model to adapt to new scenes while keeping the pretrained backbone frozen, making the process far more efficient than fine-tuning the entire network. The paper also demonstrates good generalization by applying the technique to other models like MAST3R and MonST3R.\n\nWeaknesses:\n1. The main concern is the practical cost of performing optimization at inference time. While the paper claims the method is \"nearly cost-free\", it lacks a detailed analysis of the actual test-time overhead. For any given scene, the model must run multiple forward/backward passes to optimize the prompts. This could be a significant drawback for applications requiring fast inference, and the paper does not quantify this trade-off (e.g., time per scene vs. baseline).\n2. The method relies on sampling image triplets from the test set, but the strategy for this selection is not discussed. The performance could be sensitive to which triplets are chosen (e.g., wide vs. narrow baseline pairs), how many triplets are used for optimization, and how they are sampled. This underspecified component makes the method less reproducible and its robustness unclear.", "was_revised": true, "final_justification": "The author's rebuttal has solved most of my concerns. Hence, I decided to keep my positive rating.", "rebuttal": "We thank the reviewer for the constructive comments. We provide our feedback as follows.\n\n> **`>>> W1:`**\n> **Practical cost of performing optimization at inference time.**\n---\n\n**`>>> A1:`**  Our maximum test‑time training duration is approximately 14 seconds on a single GPU. This remains consistent even with a large number of views. \n\n**Why nearly cost-free?** Test3R introduces only 14 seconds of extra time overhead, which we consider negligible during the entire process. In fact, the majority of Test3R’s time consumption comes from the pair-wise prediction and global alignment introduced by DUSt3R. This proportion increases as the number of images grows. For example, for a set of 50 images on the 7Scenes dataset, pair-wise prediction takes nearly 250s, and global alignment consumes nearly 70 seconds.\n\n> **`>>> W2:`**\n> **Strategy for triplet selection. Discussion on the performance and which triplets are chosen, how many triplets are used for optimization, and how they are sampled.**\n---\n\n**`>>> A2:`** \n* **Triplet sampling strategy.** We describe our triplets sampling strategy in Sec.A in the Appendices, and we will relocate this content to the experiment section(Sec.5). Considering a scene with $n$ images, the total number of triplets is $n^3$ for test-time training. For computational efficiency, if the number of triplets exceeds 165, we randomly sample 165 triplets as the test-time training set for each epoch.\n\nHere, we discuss the number of triplets and the sampling strategy on the Office-09 scene on the 7Scenes dataset. This scene consists of five sequential images; therefore, 125 triplets are used in each epoch.\n\n* **The number of triplets is used.** We report the results under different epoch settings to evaluate the effect of the number of triplets. As shown in the table below, Test3R significantly enhances the reconstruction quality of the scene just using one epoch for training, which is our default setting. With more epochs, the optimization leverages an increasing number of triplets, resulting in an improvement in reconstruction quality.\n\n|            Epoch            | Acc&nbsp;$\\downarrow$ | Comp&nbsp;$\\downarrow$ | NC&nbsp;$\\uparrow$ |\n|:---------------------------:|:--------------------:|:-------------------:|:--------------------------:|\n| Vanilla DUSt3R                     |     0.62        |     0.51                 |         0.54                    |\n| 1                     |     0.14        |     0.20                 |         0.69                    |\n| 2                    |        0.06     |      0.08               |                    0.70        |\n| 3                     |      0.04                |    0.04                 |          0.72                 |\n\n* **Triplets sampling**. Given that this scene is captured sequentially, short-baseline triplets are generated by increasing the frequency of source views close to the reference view. Conversely, this results in the wide-baseline experiment. As shown in the table below, the reconstruction results of these three methods are comparable. This demonstrates the robustness of our method, as it is largely insensitive to the selection of triplets.\n\n|            Epoch            | Acc&nbsp;$\\downarrow$ | Comp&nbsp;$\\downarrow$ | NC&nbsp;$\\uparrow$ |\n|:---------------------------:|:--------------------:|:-------------------:|:--------------------------:|\n| Vanilla DUSt3R                     |     0.62        |     0.51                 |         0.54                    |\n| Ours                     |     0.14        |     0.20                 |         0.69                    |\n| Wide-Baseline                     |  0.13            |  0.18                    |     0.71                        |\n| Short-Baseline                     |    0.15                  |       0.21              |     0.69                       |\n\n\n> **`>>> Q1:`**\n> **A detailed analysis of the test-time computational overhead. How many optimization iterations are performed? What is the total processing time compared to the original DUSt3R?**\n---\n**`>>> A1:`** We report the time consumption of Test3R on the Office scene with a total of 50 images. We follow our default setting and use 165 triplets to optimize Test3R for only one epoch. Therefore, there are 165 optimization iterations performed. In terms of time consumption, this training process only takes 14 seconds. After that, the pair-wise prediction and global alignment introduced by vanilla DUSt3R are performed using 320 seconds.\n\n> **`>>> Q2:`**\n> **How are the image triplets (I1,I2,I3) selected?  How does the performance of Test3R vary with different numbers of triplets or different selection strategies?**\n---\n**`>>> A2:`** Sorry for any confusion. \n\n* **Triplet sampling strategy.** We describe our triplets sampling strategy in Sec.A in the Appendices, and we will relocate this content to the experiment section(Sec.5). Considering a scene with $n$ images, the total number of triplets is $n^3$ for test-time training. For computational efficiency, if the number of triplets exceeds 165, we randomly sample 165 triplets as the test-time training set for each epoch.\n* **Performance variation.** As we mentioned in W2, the reconstruction quality improves as the number of triplets increases, and does not exhibit any significant differences when different baselines are selected."}, {"review_id": "IigSDBRfNf", "reviewer": "Reviewer_K6dW", "summary": "This paper introduces Test3R, a novel approach for performing test-time training on pairwise 3D reconstruction models, such as DUSt3R and MASt3R. The authors observe that DUSt3R produces inconsistent reference-view reconstructions when paired with different source views, which can negatively affect the final global alignment needed for a coherent 3D reconstruction. To address this issue, Test3R leverages visual prompt tuning, introducing a few learnable continuous tokens at test time. The method optimizes cross-view consistency of the reference-view pointmap as its training objective. Test3R is primarily evaluated on DUSt3R, demonstrating improvements in both multi-view depth estimation and 3D reconstruction. Additionally, the authors show that the approach generalizes to other pairwise architectures, such as MASt3R and MonST3R.", "questions": "1. At test time, are the triplets processed sequentially or in batches? If they are processed in batches, what batch size is used?\n2. The reviewer is curious whether the proposed method is compatible with using correspondences (e.g., from an off-the-shelf model), where the training objective is to enforce that points from corresponding pixels are close to each other. This extension could potentially make the approach work with multi-view methods as well.", "limitations": "The primary limitation of the proposed method is that it is currently only compatible with pairwise approaches. The authors also discuss additional limitations in the appendix.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strength:\n1. The idea of using test-time training through visual prompt tuning for 3D reconstruction is both interesting and novel. It offers an alternative to classical optimization-based methods, such as bundle adjustment, for improving 3D reconstruction at test time.\n2. The experiments show that the proposed method improves the baseline, DUSt3R on both multi-view depth estimation and 3D reconstruction. Moreover, the proposed method also generalizes well to other pairwise methods, including MASt3R and MonST3R.\n\nWeaknesses:\n1. Incomplete evaluation: The authors only conduct experiments on depth estimation and 3D reconstruction. The reviewer expects additional results on camera pose estimation.\n2. Ablation study: It would be helpful to include a comparison with (naive or LoRa-based) test-time finetuning approaches that adapt the entire model or only the encoder, without introducing extra tokens.\n3. The proposed method appears to be specifically designed for pairwise methods, whereas current state-of-the-art approaches are predominantly multi-view models, such as VGGT and CUT3R. This focus may limit the broader applicability and impact of the proposed approach.", "was_revised": true, "final_justification": "My concerns have been well resolved in the rebuttal process. I device to maintain my current score.", "rebuttal": "We would like to thank the reviewer for their insightful feedback and interesting observations. We thank the reviewer for the support of our work, and we address the reviewer’s comments below and will include all the feedback in the revised version of the manuscript.\n\n> **`>>> W1:`**\n> **Evaluation on camera pose estimation.**\n---\n\n**`>>> A1:`** Thanks for your suggestion. We provide the result on the *cave_2* scene on the Sintel dataset, and we promise that further experimental results will be conducted and included in the main paper. As shown in the table below, Test3R significantly improves the performance of DUSt3R on camera pose estimation in dynamic scenes, even though it has never been trained on any dynamic scene dataset. \n|            Method            | $ATE$&nbsp;$\\downarrow$ | $RPE_{trans}$&nbsp;$\\downarrow$ | $RPE_{rot}$&nbsp;$\\downarrow$ |\n|:---------------------------:|:--------------------:|:-------------------:|:--------------------------:|\n| DUSt3R                     |     1.12        |    0.45                  |       41.38                      |\n| Test3R                     |   **0.78**  |   **0.28**            |            **3.78**                |\n\n> **`>>> W2:`**\n> **Ablation study: It would be helpful to include a comparison with (naive or LoRa-based) test-time finetuning approaches that adapt the entire model or only the encoder, without introducing extra tokens.**\n---\n**`>>> A2:`**\n\nThanks for your suggestion. We conducted experiments on the Office-09 scene on the 7Scenes dataset. We perform fine-tuning of the DUSt3R encoder via two methods: naive fine-tuning and LoRA. The quantitative results are shown in the table below and will be included in the main paper. Specifically, by maximizing cross-pair consistency introduced in Sec.4.2, all three test-time training approaches improve reconstruction quality. However, our method, which uses prompts, significantly outperforms both the naive and LoRa-based fine-tuning approaches. \n\n|            Method            | Acc&nbsp;$\\downarrow$ | Comp&nbsp;$\\downarrow$ | NC&nbsp;$\\uparrow$ |\n|:---------------------------:|:--------------------:|:-------------------:|:--------------------------:|\n| LoRa                     |     0.45        |     0.32                 |         0.62                    |\n| Fine-tune                     |        0.28     |      0.42               |                    0.64        |\n| DUSt3R                     |      0.62                |    0.51                 |          0.54                 |\n| Ours                     |      **0.14**                |    **0.20**                 |          **0.69**               |\n\n> **`>>> W3:`**\n> **The proposed method appears to be specifically designed for pairwise methods, whereas current state-of-the-art approaches are predominantly multi-view models, such as VGGT and CUT3R. This focus may limit the broader applicability and impact of the proposed approach.**\n---\n**`>>> A3:`**\n\nThank you for pointing this out. We would like to clarify that our method remains **applicable** to multi-image input methods such as VGGT[1]. Specifically, we insert the prompts into the image tokens of each input image, in a manner similar to VGGT’s handling of the register token. During test-time training, it still takes a triplet as input to predict two pointmaps under the same reference view but paired with different source views as introduced in Sec.4.2. To validate the effectiveness of the model, we conducted an evaluation on the Office-07 scene on the 7Scenes dataset. Following the default setting in Sec.5, the number of triplets as test-time training sets is 125, and we optimize the prompt using only one epoch. The final results are shown in the table below. It indicates that our method significantly enhances both Acc. and Comp. of the pointmap predicted by VGGT[1]. This further provides evidence of the \"universally applicable\" nature of our method. This experiment will be included in the main paper.\n|            Method            | Acc&nbsp;$\\downarrow$ | Comp&nbsp;$\\downarrow$ | NC&nbsp;$\\uparrow$ |\n|:---------------------------:|:--------------------:|:-------------------:|:--------------------------:|\n| VGGT                     |     0.27        |     0.20                 |         0.71                    |\n| VGGT+Ours                     |        0.11     |      0.12               |                    0.71        |\n\n[1] VGGT: Visual Geometry Grounded Transformer.\n\n> **`>>> Q1:`**\n> **At test time, are the triplets processed sequentially or in batches? If they are processed in batches, what batch size is used?**\n---\n**`>>> A1:`** It's in batches. Batch size is set to 1 in our experimental setup.\n\n>  **`>>> Q2:`**\n> **The reviewer is curious whether the proposed method is compatible with using correspondences (e.g., from an off-the-shelf model), where the training objective is to enforce that points from corresponding pixels are close to each other. This extension could potentially make the approach work with multi-view methods as well.**\n---\n\n**`>>> A2:`** Yes. Our method is compatible with these training objective. This approach provides a strong complement to our cross-pair consistency training objective. Aligning corresponding pixels enables us to fully leverage the pointmaps of source views, as discussed in Sec.D in the Appendices. Moreover, St4RTrack [1] has demonstrated that this method contributes to improving the model’s reconstruction capability. However, this is not the primary focus of this paper. We consider it as a potential direction for future research, and sincerely appreciate your insightful suggestion.\n\n[1] St4RTrack: Simultaneous 4D Reconstruction and Tracking in the World"}, {"review_id": "XHhrGLIUb7", "reviewer": "Reviewer_dVwe", "summary": "This paper proposes Test3R, a test-time fine-tuning framework aimed at enhancing the performance of feedforward reconstruction methods such as DUSt3R. The authors observe that the output point map of the same image varies when paired with different images. To address this inconsistency, they introduce a prompt tuning strategy applied at test time. Specifically, prompt tuning is implemented by appending and fine-tuning additional tokens. Experimental results demonstrate that the proposed method improves the performance of the base models.", "questions": "Most important questions:\n- [Same as W1] Provide runtime analysis.\n- [Same as W2] How does the used prompt tuning compare to full fine-tuning or LoRA? \n- [Same as W4] Directly compare on image pairs with our without test time fine-tuning.\n \n\n- [Q1] The method is limited to handling three input views. However, in Sec. 5.1, experiments are conducted with 3–5 and 2–4 views. How is this discrepancy addressed?\n- [Q2] Can the method be extended to handle a larger number of input views, such as more than 10?\n- [Q3] In Fig. 1, the point maps are aligned using only ICP, as DUST3R outputs lack consistent scale. This undermines the reliability of the visualizations. Additionally, no comparable visualization of Test3R is provided.", "limitations": "yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths**:\n\n- Test-time fine-tuning is an effective approach for improving the performance of feedforward reconstruction models.\n- The proposed method consistently demonstrates improvements over baseline models.\n\n**Weaknesses**:\n\n- [W1] The authors repeatedly claim that their method is nearly cost-free; however, prompt tuning clearly incurs computational cost. No runtime analysis or comparison is provided.\n    \n- [W2] The motivation for using prompt tuning is not sufficiently ablated. While test-scene fine-tuning can naturally improve performance, it remains unclear why prompt tuning is specifically needed. There are lots of other fine-tuning methods that can be used, such as fine-tuning or LoRA.    \n\n- [W3] The authors claim the method is “universally applicable and nearly cost-free,” yet all experiments are conducted on DUSt3R/MASt3R/MonST3R, which share the same architecture. It is unclear how well the method generalizes to other models like CUT3R or VGGT, which support more input images.\n\n- [W4] Since the method operates directly on image pairs, qualitative results showing outputs before and after applying Test3R would help demonstrate its effectiveness.\n\n- [W5] Other writing issues:\n  - Line 39 typo: \"reconstruction.\"\n  - Section 4.1 is redundant and overlaps with content in Lines 31–43.\n  - The method operates on only two image pairs, making the ellipsis in the left part of Fig. 2 unnecessary.\n  - Line 232 mentions using DUST3R as the backbone, while Line 64 refers to MAST3R and MonST3R. This inconsistency should be clarified.", "was_revised": true, "final_justification": "Thank you for the authors' reply. I will raise my score and recommend that, in the revised manuscript, you revise the “cost-free” claim and clearly state the actual time-consuming nature.", "rebuttal": "Thank you for your positive feedback and insightful comments! Below, we respond to address your concern. We welcome further discussion to enhance the clarity and effectiveness of our work.\n\n>  **`>>> W1:`**\n>  **The authors repeatedly claim that their method is nearly cost-free; however, prompt tuning clearly incurs computational cost. No runtime analysis or comparison is provided.**\n---\n**`>>> A1:`** We appreciate your suggestion and apologize for the oversight. The test-time optimization process is relatively short, taking approximately 14 seconds per scene on average with a single GPU. Regarding GPU memory usage during pair-wise prediction, Test3R consumes 2.57 GB, compared to 2.54 GB for vanilla DUSt3R. We consider this additional memory footprint negligible.\n\n>  **`>>> W2:`**\n>  **The motivation for using prompt tuning is not sufficiently ablated. While test-scene fine-tuning can naturally improve performance, it remains unclear why prompt tuning is specifically needed. There are lots of other fine-tuning methods that can be used, such as fine-tuning or LoRA.**\n---\n\n**`>>> A2:`**\nThanks for your suggestion. We conducted experiments on the Office-09 scene on the 7Scenes dataset. We perform fine-tuning of the DUSt3R encoder via two methods: full-parameters fine-tuning and LoRA. The quantitative results are shown in the table below and will be included in the main paper. Specifically, by maximizing cross-pair consistency introduced in Sec.4.2, all three test-time training approaches improve reconstruction quality. However, our method that uses prompts significantly outperforms both the full-parameters and LoRa-based approaches.\n|  Method   | Acc&nbsp;$\\downarrow$ | Comp&nbsp;$\\downarrow$ | NC&nbsp;$\\uparrow$ |\n| :-------: | :-------------------: | :--------------------: | :----------------: |\n|   LoRa    |         0.45          |          0.32          |        0.62        |\n| Fine-tune |         0.28          |          0.42          |        0.64        |\n|  DUSt3R   |         0.62          |          0.51          |        0.54        |\n|   Ours    |         0.14          |          0.20          |        0.69        |\n\n>  **`>>> W3:`**\n>  **The authors claim the method is \"universally applicable and nearly cost-free\" yet all experiments are conducted on DUSt3R/MASt3R/MonST3R, which share the same architecture. It is unclear how well the method generalizes to other models like CUT3R or VGGT, which support more input images.**\n---\n\n**`>>> A3:`** Thank you for pointing this out. We would like to clarify that our method remains **applicable** to multi-image input methods such as VGGT[1]. Specifically, we insert the prompts into the image tokens of each input image, in a manner similar to VGGT’s handling of the register token. During test-time training, it still takes a triplet as input to predict two pointmaps under the same reference view but paired with different source views as introduced in Sec.4.2. To validate the effectiveness of the model, we conducted an evaluation on the Office-07 scene on the 7Scenes dataset. Following the default setting in Sec.5, the number of triplets as test-time training sets is 125, and we optimize the prompt using only one epoch. The final results are shown in the table below. It indicates that our method significantly enhances both Acc. and Comp. of the pointmap predicted by VGGT[1]. This further provides evidence of the \"universally applicable\" nature of our method. This experiment will be included in the main paper.\n|  Method   | Acc&nbsp;$\\downarrow$ | Comp&nbsp;$\\downarrow$ | NC&nbsp;$\\uparrow$ |\n| :-------: | :-------------------: | :--------------------: | :----------------: |\n|   VGGT    |         0.27          |          0.20          |        0.71        |\n| VGGT+Ours |         0.11          |          0.12          |        0.71        |\n\n[1] VGGT: Visual Geometry Grounded Transformer.\n\n\n>  **`>>> W4:`**\n>  **Since the method operates directly on image pairs, qualitative results showing outputs before and after applying Test3R would help demonstrate its effectiveness.**\n---\n\n**`>>> A4:`** We apologize we are currently unable to provide similar visualizations here due to NeurIPS policy restrictions. However, we provide a comparison of depthmap consistency in Fig. 5 in the Appendices. This comparison can also illustrate the improvement in pointmap consistency, according to Eq.9. As shwon in this Figure, our model effectively improves cross-pair consistency, and generates consistent and reliable depth in regions with limited overlap. We will supplement the visualization results of the pointmap in subsequent versions.\n\n>  **`>>> W5:`**\n>  **Other writing issues.**\n---\n**`>>> A5:`**\n\nThanks for your suggestions for our writing issues; they are helpful in improving the clarity and readability. \n* **Line 39 typo: \"reconstruction.\"**\n    * We will revise it.\n* **Section 4.1 is redundant and overlaps with content in Lines 31–43.**\n    * We will reorganize these two parts accordingly. An overview will be provided in the Introduction section, followed by a detailed description in Section 4.1.\n* **The method operates on only two image pairs, making the ellipsis in the left part of Fig. 2 unnecessary.** \n    *  We will revise the figure.\n* **Line 232 mentions using DUST3R as the backbone, while Line 64 refers to MAST3R and MonST3R. This inconsistency should be clarified.** \n    * We would like to clarify that the experiments in Sec.5 are all based on the DUSt3R(Line232). We applied Test3R to MASt3R and MonSt3R to demonstrate the universal applicability of our model(Line 64). We apologize for the misunderstanding and will correct the description in Line 232.\n\n>  **`>>> Q1:`**\n>  **The method is limited to handling three input views. However, in Sec. 5.1, experiments are conducted with 3–5 and 2–4 views. How is this discrepancy addressed?**\n---\n\n**`>>> A1:`** Sorry for any confusion. We would like to clarify that our model can handle an arbitrary number of images. The input during test-time training is three images. Specifically, in Sec.5.1, we consider all input viewpoints to construct the triplets. Subsequently, these triplets serve as the test-time training set to optimize our model.\n\n>  **`>>> Q2:`**\n>  **Can the method be extended to handle a larger number of input views, such as more than 10?**\n---\n\n**`>>> A2:`** **Yes.** Our model can handle an arbitrary number of images.\n\n>  **`>>> Q3:`**\n>  **In Fig. 1, the point maps are aligned using only ICP, as DUST3R outputs lack consistent scale. This undermines the reliability of the visualizations. Additionally, no comparable visualization of Test3R is provided.**\n---\n\n**`>>> A3:`** Thanks for pointing this out. In fact, we consider scale inconsistency to be **a specific part of cross-pair inconsistency**. If the model can fully adapt to the scene, the scale of its pointmaps would also be consistent. Therefore, we only use ICP to align the pointmap. \n\n**Visualization.** Due to NeurIPS policy restrictions, similar visualizations of Test3R are not able to be included in the rebuttal phase. We will supplement the visualization results of the pointmap in revision. We would like to kindly invite you to refer to Fig.5 in the appendix, which provides a comparison of cross-pair consistency on the depthmap. It can reflect our improvement for pointmap consistency, according to Eq.9."}, {"review_id": "NRBdWguHsy", "reviewer": "Reviewer_aSNX", "summary": "This paper proposes Test3R, which improves 3D reconstruction accuracy through test-time training. Specifically, it optimizes consistency across image pairs using a triplet consistency objective, enhancing overall reconstruction quality. The method employs prompt tuning, adjusting only the prompt parameters, resulting in relatively low computational cost. Test3R surpasses some previous methods across several challenging datasets and tasks without relying on camera parameters. Test3R demonstrates strong generalization capabilities and can be integrated into various reconstruction frameworks based on DUSt3R.", "questions": "All my concerns have been detailed in the weaknesses section.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "# Strengths\n\n1. The paper is clearly written and easy to follow.\n\n2. The technical design of the proposed method is reasonable.\n\n# Weaknesses\n\n1. Although the triplet consistency-based optimization objective is technically sound, it is not an optimal approach since it does not fully exploit the information available in test scenes. A more effective approach might directly leverage images for optimization, as demonstrated by methods like 3DGS.\n\n2. Recent feed-forward approaches such as VGGT can handle reconstruction with multiple input views without needing pair-wise depth estimation, theoretically avoiding the inconsistency issues discussed regarding DUSt3R. However, the authors lack discussion and comparison with such methods.\n\n3. The proposed method requires per-scene optimization, which, even when tuning only part of the parameters, inevitably reduces speed. The paper seems to lack a thorough analysis and comparison of reconstruction speed, which is critical. If the method is slow, it would be beneficial for the authors to include comparisons with other per-scene optimization methods such as some 3DGS based methods.\n\n4. Overall, the proposed approach is relatively simple, essentially adding per-scene optimization as a post-processing step to DUSt3R. Moreover, the chosen optimization objective is straightforward but clearly suboptimal, and the Visual Prompt Tuning component essentially utilizes a mature fine-tuning strategy. Thus, I consider the novelty and technical contribution of this paper to be very limited.\n\n5. There are minor issues, such as the typo \"recomstruction\" on line 39.", "was_revised": true, "final_justification": "Thanks for the authors’ efforts in the rebuttal. I am impressed by the significant performance improvements achieved when integrating with VGGT, as well as the remarkably short optimization time of only 14 seconds. I decide to raise my score to borderline accept. And I would recommend the authors to incorporate some of key points mentioned in rebuttal into the the paper.", "rebuttal": "We thank the reviewer for the constructive comments. Below, we provide a point-by-point response to address your concerns. We welcome further discussion to improve the clarity and effectiveness of our work.\n> **`>>> W1:`**\n> **Not an optimal approach. A more effective approach might directly leverage images for optimization, as demonstrated by methods like 3DGS.** \n---\n\n**`>>> A1:`** \n* **3DGS-based model.** We would like to clarify that our method and 3D Gaussian Splatting (3DGS) serve different purposes and are based on fundamentally different design principles. 3DGS is designed for photorealistic appearance reconstruction through iterative photometric optimization. In contrast, our approach focuses on accurate geometric prediction using feed-forward models, similar to DUSt3R [1], MAST3R [2], and VGGT [3]. These methods play a role similar to Structure-from-Motion (SfM) in the reconstruction pipeline. Therefore, direct comparisons with methods like 3DGS are not fully aligned in scope. Importantly, our method can provide accurate geometric initialization and camera poses, which can serve as a strong complement for iterative 3D reconstruction models such as 3DGS and NeRF.\n\n* **NOT suboptimal**. Our method is designed to achieve optimal geometric prediction under the feed-forward paradigm. By enforcing geometric consistency across triplets, our method leverages the mutual constraint between partial reconstructions, which is a fundamental signal in scene geometry. In an ideal scenario with perfect reconstruction, this consistency loss would naturally vanish. This principled design allows our model to generalize across scenes with minimal testing-time optimization, making it efficient and robust in practice.\n\n[1] DUSt3R: Geometric 3D Vision Made Easy.\n\n[2] Grounding Image Matching in 3D with MASt3R.\n\n[3] VGGT: Visual Geometry Grounded Transformer.\n\n> **`>>> W2:`**\n> **Discussion and comparison with recent feed-forward approaches such as VGGT.**\n---\n\n**`>>> A2:`**\n\n* **Comparison**: We have provided a comparison with two feed-forward approaches, VGGT[1] and Fast3R[2], in the Tab.6 in the Appendices. As shown in this table, Test3R achieves significantly superior reconstruction quality compared to Fast3R. Moreover, it outperforms VGGT across the vast majority of evaluation metrics.\n\n* **Discussion**: While VGGT processes multiple views jointly and avoids pairwise depth estimation, it still suffers from cross-pair inconsistency in predicted geometries. We would like to highlight that this inconsistency can still be leveraged through our method to enhance the reconstruction quality of VGGT. To demonstrate this, we implemented our method on VGGT. Specifically, we insert the prompts into the image tokens of each input image, like the operation of VGGT’s handling of the register token. During test-time training, it still takes a triplet as input to predict two pointmaps under the same reference view but paired with different source views as introduced in Sec.4.2. We evaluated it on the Office-07 scene on the 7Scenes dataset. The final results are shown in the table below. It indicates that Test3R significantly enhances both Acc. and Comp. of pointmap predicted by VGGT[1].\n\n|  Method   | Acc&nbsp;$\\downarrow$ | Comp&nbsp;$\\downarrow$ | NC&nbsp;$\\uparrow$ |\n| :-------: | :-------------------: | :--------------------: | :----------------: |\n|   VGGT    |         0.27          |          0.20          |        0.71        |\n| VGGT+Ours |         0.11          |          0.12          |        0.71        |\n\n[1] VGGT: Visual Geometry Grounded Transformer.\n\n[2] Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass\n\n> **`>>> W3:`**\n> **A thorough analysis and comparison of reconstruction speed. Comparisons with other per-scene optimization methods, such as some 3DGS-based methods.**\n---\n\n**`>>> A3:`** \n* **Reconstruction speed.** We appreciate the reviewer’s concern about runtime. The optimization process is relatively short, taking approximately 14 seconds on a single GPU. The majority of Test3R’s time overhead comes from the pair-wise prediction and global alignment in the original DUSt3R's pipeline, which together take 320 seconds for a set of 50 images on the 7Scenes dataset.\n\n* **Runtime comparison with 3DGS.** While these two methods are not directly comparable as discussed in W1,  our method only introduces a few seconds of extra overhead; the total reconstruction time remains much shorter than that required by 3DGS to reconstruct a large-scale scene, which typically takes about an hour.\n\n  We will include this discussion and timing statistics in the revision for clarity and completeness.\n\n> **`>>> W4:`**\n> **The limited novelty and technical contribution of this paper.**\n---\n**`>>> A4:`** We thank the reviewer for this question. It gives us a valuable opportunity to clarify our design.\n\nYes, our method is simple, but this simplicity is intentional and highlights our strength. Simplicity does not diminish novelty; rather, it underscores the effectiveness of our insight.\n\nSpecifically, Test3R is novel in both **new paradim** and **technical application**:\n\n1. **A New Paradigm, Not Post-Processing**: We are the first to introduce TTA to feedforward 3D reconstruction. This shifts from static inference to dynamic, per-scene model adaptation. Crucially, this is **not post-processing**, which operates on fixed outputs. Instead, we dynamically adapt the model's parameters for each scene during inference, which is fundamentally different from refining a static result.\n\n2. **Technical novelty**: \nOur technical approach enables this new paradigm with two key contributions:\n- **Self-consistency as new objective.**\nOur objective is straightforward by design, but it is **not suboptimal**. It is based on the universal principle of geometric self-consistency: any partial reconstructions of the same scene must be consistent with one another. For a perfect reconstruction, this self-consistency loss would naturally be zero. This self-consistency loss is never introduced in any paper before.\n- **First use of VPT in 3D**\nWhile VPT is an established PEFT method, we are the first to apply it to feedforward 3D models under TTA. Adapting visual prompts for dense 3D geometry prediction is non-trivial and requires careful integration.\n\nWe will highlight those points in the revision. Thanks!\n> **`>>> W5:`**\n> **There are minor issues, such as the typo \"recomstruction\" on line 39.**\n---\n**`>>> A5:`**\n\nWe appreciate your reminder and will revise it."}]

Author rebuttal:

Meta-review: The paper received 4 reviews. The authors-reviewers discussion clarified most points raised in the initial reviews. A consensus that the paper brings a contribution to 3D reconstruction and brings a method with good performance was reached (in both reconstruction quality and computation time), and that the paper can be revised to meet the requirements for publication based on the authors-reviewers discussion. The scores were revised accordingly. The paper could be accepted as poster.
Decision: Accept (poster)