Eve3D: Elevating Vision Models for Enhanced 3D Surface Reconstruction via Gaussian Splatting
Authors: Jiawei Zhang
Youmin Zhang
Fabio Tosi
Meiying Gu
Jiahe Li
Xiaohan Yu
Jin Zheng
Xiao Bai
Matteo Poggi
Venue: NeurIPS 2025 poster ()
Keywords: surface reconstruction
3d gaussian splatting
Primary area: applications
TL;DR: This paper presents Eve3D, jointly optimizing 3d gaussian splatting with prior depth maps to efficiently reconstruct accurate surfaces.

Abstract: We present Eve3D, a novel framework for dense 3D reconstruction based on 3D
Gaussian Splatting (3DGS). While most existing methods rely on imperfect priors
derived from pre-trained vision models, Eve3D fully leverages these priors by
jointly optimizing both them and the 3DGS backbone. This joint optimization
creates a mutually reinforcing cycle: the priors enhance the quality of 3DGS, which
in turn refines the priors, further improving the reconstruction. Additionally, Eve3D
introduces a novel optimization step based on bundle adjustment, overcoming the
limitations of the highly local supervision in standard 3DGS pipelines. Eve3D
achieves state-of-the-art results in surface reconstruction and novel view synthesis
on the Tanks & Temples, DTU, and Mip-NeRF360 datasets. while retaining fast
convergence, highlighting an unprecedented trade-off between accuracy and speed.

Eve3D: Elevating Vision Models for Enhanced 3D
Surface Reconstruction via Gaussian Splatting
Jiawei Zhang
Youmin Zhang2t Fabio Tosi Meiying Gu
Jiahe Li!
Xiaohan Yu
Jin Zheng! Xiao Bai Matteo Poggi*
School of Computer Science and Engineering, State Key Laboratory of Complex Critical Software Environment, Jiangxi Research Institute, Beihang University State Key Laboratory of Virtual Reality Technology and Systems, Beijing UUiveesitty of Bologna MMaqquari University
*Rawmantic Al
Synthesis
Refermce
20G8
GDF
Eve3D
Depth
Figure Geometrie Reconstruction and Rendering. Examples from a challenging scene showing our Eve3D method's s accurate geometry and appearance. Left: Normal maps highlight superior surface reconstruction over 2DGS [8] and GOF [55], particularly on flat surfaces and object boundaries. Right: Photorealistic view synthesis alongside its detailed depth map rendering.
Abstract
We present Eve3D, novel framework for dense 3D reconstruction based on 3D Gaussian Splatting (3DGS). While most existing methods rely on imperfect priors derived from pre-trained vision models, Eve3D fully leverages these priors by jointly optimizing both them and the 3DGS backbone. This joint optimization creates mutually reinforcing cycle: the priors enhance the quality of 3DGS, which in turn refines the priors, further improving the reconstruction. Additionally. Eve3D introduces novel optimization step based on bundle adjustment, overcoming the limitations the highly local supervision in standard 3DGS pipelines. Eve3D achieves state-of-the-ee results in surface reconstruction and novel view synthesis on the Tanks & Temples, DTU, and Mip-NeRF360 datasets. while retaining fast convergence, highlighting an unprecedented trade-off between accuracy and speed.
Introduction
Dense 3D scene reconstruction is a crucial task in computer vision and graphics, supporting ap- plications ranging from virtual reality and simulating environments to robotic navigation. Recent breakthroughs in this field draw inspiration from the adjacent literature concerning novel view
'This work conducted during Jiawei Zhang's 's research internship at Rawmantic AL
Project Lead
t "oorrespnniigg author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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synthesis [32 22). which faced a remarkable revolution in the last few years, Initially, Neural Radi- ance Fields (NeRFs) [2] were adapted encode dense 3D surfaces within MLP weights 42 25
however, despite their compact representation, these approaches impose prohibitive computational demands, often requiring hundreds of hours of processing to reconstruct a single scene, More recently, 3D Gaussian Splatting [22] (3DGS) has gained significant attention in the graphics community,
characterized by an optimized rasterizer that facilitates real-time rendering, establishing it as the current preferred alternative to NeRFs. This has led to the development of numerous JDGS-based approaches 18 [55 6 [2]] that deliver promising results and high-fidelity scene reconstructions. However, despite the efficiency and flexibility of 3DGS and the latest advances, the framework continues to face significant limitations that hinder its broader application for 3D reconstruction task.
First and foremost, 3DGS in its original formulation is unsuited for this purpose. as Gaussian primitives rarely fit accurately to true surface geometries [16). This limitation derives from two critical factors: the inherent independence of Gaussian primitives, which operate without contextual awareness, and the sole reliance on image reconstruction losses during training. While this approach excels at novel view synthesis, it often fails at properly modeling the real 3D geometry of the observed scene, Second, the accuracy of 3DGS heavily relies on a proper initialization strategy for seeding Gaussian primitives. Despite its efficiency and widespread adoption, COLMAP's 35] reliance on local feature makes it vulnerable to challenging scenarios such as textureless regions and repetitive patterns, resulting in structural distortions, blurring, and under-reconsttrueed areas.
To address these shortcomings, recent 3D reconstruction pipelines built upon 3DGS 18
55
29
have incorporated additional scene priors from pre-trained vision models 48
58 5
12
15
53. Among these supplementary signals, depth and surface normals have proven most effective, significantly enhancing reconstruction quality in traditionally challenging scenarios such as textureless regions. On the one hand, although vision models can reason both locally and globally over their inputs, they are constrained by the limited amount of images they can process simultaneously e.g.. typically a single frame [48] 12 15 53 a stereo pair [58 45 3 or at most about ten images 5]. This limitation restricts these models to analyzing only localized portions of scene when extracting priors. Furthermore, despite their training on large-scale datasets ranging from hundreds of thousands [4 to several million images [48]. the priors they predict still suffer from inaccuracies. These dual constraints create sub-optimal supervision for 3DGS, tampering with the optimization process and ultimately limiting the final 3D) reconstruction accuracy.
In this paper, we introduce Eve3D, a novel 3D reconstruction framework built upon 3D Gaussian Splatting, specifically designed to overcome these limitations. Firstly, Eve3D unlocks the full potential of vision models by rendering synthetic rectified stereo pairs through 3DGS and applying ofth-offthee-rt stereo foundation models to obtain geometric priors. These priors are then refined through joint optimization alongside the 3DGS model itself. This joint optimization strategy works by pooopaaopgg gradients through both the depth rendered by 3DGS and the predicted depth priors, with the latter treated as learnable parameters throughout the process. Secondly, we propose a local bundle adjustment strategy that maintains global consistency across co-visible frames during each forward optimization. This approach overcomes 3DGS's inherent limitation in simultaneously rasterizing and optimizing multiple frames- constraint imposed by computational complexity. This allows Eve3D to achieve unprecedented accuracy, as demonstrated in Fig. 1
Eve3D is trained and evaluated over popular benchmarks for 3D reconstruction, including DTU g and Tanks and Temples (24), achieving state-of-the-art accuracy on both datasets. In particular, on the latter, this is done in just 20 minutes, while it takes only ~ 1 GPU hours to push the accuracy to the upper bound. In summary, the main contributions of this paper are:
We introduce Eve3D, novel framework for dense 3D reconstruction based on 3DGS, setting a new state-of ohethe-art in the field while maintaining efficient training time.
We develop an improved supervision paradigm for 3DGS used to train Eve3D, which treats external priors as learnable parameters and optimizes them jointly with the 3DGS model during training.
We introduce a local bundle adjustment to better enforce multi-view consistency during each optimization step, overcoming one of the main limitations of 3DGS-basel frameworks.

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2 Related Work
Neural Scene Representations. Neural scene representations have revolutionized 3D reconstruction and rendering. NeRF [32] pioneered this direction with continuous volumetric functions modeled by MLPs, though at high computational cost. More recently, 3DGS [2 introduced an explicit representation using 3D Gaussian primitives with efficient rasterization based rendering, achieving superior quality and real-time performance. Despite successful applications in large-scale scene
reconstruction 23 28 26 SLAM systems (47 21 37 63 40), dynamic scene modeling 4o (0
46 27], Al-generated content [38 & (34 61, and autonomous driving [62 30 66), both NeRF and
3DGS focus primarily on appearance rather than geometry, resulting in poorly defined surfaces.
Neural Surface Reconstruction. To address these limitations, several methods have extended neural rendering for accurate surface reconstruction. NeuS [2] and VolSDF [52] represent surfaces as
zero-level sets of signed distance functions without mask supervision. Neuralangelo [25] enhances
detail with multi-resolution hash grids and numerical gradients, while NeuralWarp [TO] improves
consistency through patch warping. These implicit approaches produce high-qualing surfaces but
require significant computational resources and long optimization times.
Surface Reconstruction with Gaussian Splatting. 3DGS's efficiency has inspired many surface reconstruction methods. SuGaR [66 introduced regularization for surface-aligned Gaussians, en- abling Poisson reconstruction. 2DGS [88 uses planar disks for surface modeling, while Gaussian Surfels [9] treats local z-axis as normal direction. GS2Mesh [45] extracts meshes using TSDF fusion
on depth maps from pre-trained stereo model, while StereoGS 33 employs self-improving depth supervision with virtual stereo pairs. Some approaches combine Gaussians with implicit fields: GOF [55] derives an opacity field, while GSDF [54] and 3DGSR [1 integrate 3DGS with signed distance functions. Others focus on geometric constraints, like PGSR 6 with unbiased depth rendering, DN-Splatter [41] with depth/normal priors, and VCR-GauS 7 with view-consistent depth- normal regularization. GS-Pull [57] aligns Gaussians to a neural SDF's zero-level set. Despite progress, current approaches have limitations with geometric priors either using external vision model priors applying heuristic constraints that struggle with complex geometries. Our work jointly optimizes Gaussians and priors in a unified framework, introducing non-local information flow for better geometric consistency.
that may be inconsistent 48 58
5 12 15 53
3 Method
3.1 Framework Overview
Scene Representation. As illustrated in Fig. 2 our backbone is built over 3D Gaussian Splatting, which models the scene as a set of Gaussian primitives, each one defined as:
(a(a), Σ.) 1 [1)= T (a
(I)
with μ4 E 3 and Σg E 3x3 being the center and 3D covariance matrix, respectively. The latter can be decomposed into scaling and rotation matrices S, R, E R3333
Σ, R.S.S. R.
(2)
To better fit surfaces 18] 9 we enforce Gaussians to be flat by minimizing the minimal factor in S, The pixel-wise color C' E R is rendered through a-blending:
i-1
C= Σ Tracc T. (1-
(3)
SEN
3=1
where a and E R33 are the alpha and view view-dependent color. Similarly, properties such as depth and surface normals be rendered. Following (6), normals N are derived from the minimum scale factor direction n, and camera rotation R,, Depth D are obtained through unbiased rendering (6. calculated as the intersection between the ray and the plane defined by rendered normals and the distance map D, where d; = (R- (1: (f4, To)) (R. n.) represents the distance from the plane the camera center T., The two geometric properties follow:
N Σ ie
R n,a, II (1
ay), D= Σ d, I
(1 - a,).
(4)
1=1
3

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(g) Uptimization
Adjastment
Canea
Irago
Rem
Cosspiency
Consiaency
3DG5 Readeee
ocal Bandle Adjestment
Nerd hoe
Learable Depth
Prices
3DGS
Meding
Morching Network
Depth Camputation
Isital Dopth Pars
lnttal Namnal Price
Figure 2: Overview of our Framework. (a) We generate stereo pairs by rendering from virtual camera using 3DGS. (b) A pre-traines stereo network extracts initial depth priors. (c) Our core contribution: joint optimization of learnable depth priors and 3DGS, enhanced by local bundle adjustment for geometric and photometric consistency. (d) Final results showing high-quality mesh reconstruction, accurate depth/normal maps, and high-fidelity novel view synthesis.
While color can supervised with real images, depth and normals can supervised using priors from pre-trained vision models [48] 58 5
15 53 for better optimization.
Priors Computation. Unlike approaches supervising 3DGS with ill-posed monocular depth models we use binocular stereo matching (39] for more geometrically consistent priors. Since our method operates in a standard multi-view setting with single moving camera rather than a stereo rig, we exploit 3DGS's s view synthesis to render rectified stereo pairs [33] [55. Specifically, for a given camera pose Pt, we define virtual right camera with pose P,, at a distance as:
48
12.
P ( x.P.P
1DP with t (b 0 0)
(5)
We render virtual right image and form stereo pair with the input left image (I, We then apply pre-trained otate-of-the--at stereo model - FourdationStereo [44 in our implementation to predict disparity map, which converted to depth D* through triangulation. We also derive a confidence mask M by computing the consistency check between disparity maps for left and right views. While effective for assisting 3DGS optimization (33) and recovering meshes [45], we argue that explicitly addressing the noisy nature of these priors is critical to fully exploit their potential.
3.2 Prior-Involved Local Bundle Adjustment
Multi-view consistency vital for determining accurate surfaces. However, extracting accurate and detailed surfaces from either Gaussians or vision models presents challenges without multi-view constraints. This difficulty arises because 3DGS captures depth information through image rendering supervision, while vision models inherently contain noise and have limited view inputs. To address this issue, we have developed a local bundle adjustment algorithm that utilizes both rendered and prior depth maps, enhancing their multi-view consistency to represent surfaces more accurately. In multi-view pairs, we employ priors at source views, which allows us to incorporate more viewpoints in single loop by climinating the need for depth rendering. For the reference view, select either the rendered or prior depth map, depending on the stage of joint optimization (See 3.3
For view V, at the current iteration, we build factor factor-graph To balance reconstruction quality and efficiency, rather than optimizing all frames together, we only select views that overlap with the current view i. Specifically, following standard MVS methods [51 4 [ we determine neighboring views by computing an overlapping score and select only the top K frames (V, (V}) We construct the graph by adding edge connections between the current view Vi and each of neighboring views V,. The underlying optimization principle enforces both geometric and photometric consistency.
graph (V, E) to perform local bundle adjustment.

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Geometric Consistency. Given learnable depth D, of the current view V,, we first convert it into normal map N D. with finite differences as in [88] and derive the distance map D; as:
(6) where p is the 2D position on the image plane, p denotes its homogeneous coordinate, and K; the camera intrinsic matrix. We then map the full set of pixel coordinates P. from the current view V to the neighboring view Vj through the homography matrix Hij:
D. = D.))) (p)
TON D.
HH HG (R,;
(7)
D:
where Ra and T. are the relative rotation and translation from view V; to the neighboring view Vi- Similarly, for pixels in the neighboring view Vj, we derive the surface normal and distance map from its learnable depth Dj to compute the homography matrix Hji- By enforcing geometry consistency, we aim at minimizing the projection error min Pij. where
P = P. H,
(8)
Photometric Consistency. This constraint is based on plane patches. For pixels P: in the current view V,, we map a 7 x 7 pixel patch centered at each pixel p E P. to the neighboring view using the homography matrix Hıj. We aim to minimize the photometric error zero:
V = (1 (I, I, (H),
(9)
Objective. To account for occlusions between views and noise in depth estimates, following [6) we model the confidence map to weight the error function Wij and the overall cost function to refine depth maps of the current view and its neighbors is defined as:
Liba
Abba
Σ
W.e (Ay 2 + M.).
(10)
(a
3.3 Joint Optimization of 3DGS and Priors
Regardless of their source, all model-generated priors inevitably contain noise and inaccuracies. Rather than treating them rigid supervision, we jointly optimize both the 3DGS model and the associated guidance. This creates a mutually beneficial relationship. refining priors through multi-view consistency while providing improved supervision for the 3DGS representation.
Given prior depth map D*, we initialize set of learnable parameters Đ with its values. The joint optimization follows a two-phase schedule. Initially (i.e., before iteration Tjinnt), we use the original D* to supervise 3DGS and avoid early convergence to poor local minima. Later, once the jointly with 3DGS parameters through backpropagation, allowing the model to refine both the scene representation and the the guidance signal simultancously without degrading their quality.
Parameterized Prior.
depth becomes sufficiently reliable, switch optimizing Đ
Local Bundle Adjustment Pre-training, Neural networks that generate prior depth estimates often do at very sparse viewpoints, resulting in insufficient multi-view consistency. To address this limi- tation, before starting the joint optimization (Tjoint), we apply our proposed local bundle adjustment between the prior depths of different viewpoints, which will provide multi view consistent prior initialization before starting the joint optimization of 3DGS and prior depth.
Confidence Mask Update. As the quality of depth prior gradually improves through the joint optimization and local bundle adjustment, the initial confidence mask M may become outdated. Concurrently, the confidence map computed during local bundle adjustment reflects the quality of the latest depth prior. Therefore, we update the mask M
M M, V (Wy 0) for j (1,2, .K}
(11)
For pixels with value 0 in M i.e., low-quality depth prior, it remains possibile for them to be adjusted to more accurate position by 3DGS:
L Apuu(~ | D deach l1,
(12)
where dettach denotes the gradient of rendered depth D detached. When such pixels attain a sufficiently accurate state and are subsequently classified as confident by local bundle adjustment, they become eligible to participate in joint optimization and local bundle adjustment, ultimately contributing to accurate surface reconstruction.
5

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Table I: Quantitative results of F1 Score Tanks and Temples. absolute, second, and third bests respectively.
indicate the
Coneriilaar
Courthouse
Egratius
Meetingroom
1
Implici
27 Neuralangcio
5-120
Eoplain
0.16
Exz (Qurs)
Eve3D (Ouns)
0.20
0.35
0,96
0.56
1.2h
Reference Image
2DGS 18
GOF 5S
PGSR 6
Eve3D (Ours)
Figure 3: Qualitative Comparison on Tanks and Temples. We visualize the surface normal of reconstructed 3D meshes for comparison.
Training Loss
With the prior depth, we apply single-view prior loss Ldepth to regularize the depth and normal from 3D Gaussians in confident regions weighted by Mc:
D -DIi, iter < Tjoints
Cprior
M @
(13)
DI1a, otherwise.
To strengthen the impact plane surface reconstruction, we also jointly optimize the normals obtained from 3DGS and the depth prior in confident regions as follows:
I(WD.. -WWD=, iter < Toint (WW N), otherwise,
Chormal
M O
(14)
where T(A, B) denotes the pixel-wise cosine distance (1 A B) between normal maps. In addition, we also use ground-truth color loss L. [22), depth-normal consistency loss Lat encourage consistency between rendered depth and rotational normal (6] [8 (35), and scale loss L. 66 to encourage Gaussians to flatten to planes. The final training loss is:
Le + Lan + L. + Cprior Liba Lpult.
(15)
4 Experiments
4.1 Experimental Setup
Datasets.
We evaluate on real-world datasets, including object-centric, indoor, and outdoor scenes. For 3D reconstruction, we use large-scale scenes from Tanks and Temples (44 and 15 object-centric
scenes from DTU 20 For novel view synthesis, we use the Mip-NeRF360 dataset
Evaluation Metrics. Following established protocols, we assess reconstruction accuracy using Chamfer Distance (CD) DTU [9] and F-score Tanks and Temples 244, employing the official evaluation scripts. For novel view synthesis evaluation on Mip-NeRF360, we use standard rendering
quality metries: PSNR, SSIM
4 and LPIPS [56].
Baselines. We compare against state-of-the-art methods from two categories: (1) implicit NeRF
based approaches including NeRF (32) VoISDF [52). NeuS 442. Geo-Neus [14] NeuralWarp 10
and Neuralangelo [25;: and (2) explicit 3DGS baaed frameworks including 3DGS (2). SuGaR 16
DN-Splatter 41). GSurfels 19]. 2DGS [18), GOF (55). GS2Mesh [45), PGSR 6 and GS- GS-Pull 57

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Table 2: Quantitative Results (Chamfer Distance) DTU.
indicate the absolute,
second, and third bests respectively.
Method
105
110
118
122
Mein
Time
NeRF E93
1.90
1.60
1.85
0.58
2.28
1.27
1.47
1.67
2.05
1.07
0.88
2.53
1.06
1.15
0.96
1.49
-2h
Impheet
VoISDF 13
1.14
1.26
0.81
0.19
1.25
0.70
0.72
1.29
1.18
0.70
0.66
1.08
0.42
0.61
0.55
0.36
3-12h
(22
2-12h
Noural Warp
0.49
0.71
0.38
0.388
0.79
03.81
0.82
1.20
1.06
0.66
0.74
0.4
0.63
0.51
0.68
5-0h
Neuralangelo
25
0.37
0.72
0.35
0.35
0.87
0.55
0.53
1.29
0.97
0.73
0.47
0.74
0.32
0.41
0.43
00.61
2-12h
3DGS bi
2.14
1.53
2.08
1.68
3.19
2.21
1.43
2.07
2.22
1.75
1.79
2.55
1.53
1.52
1.50
1,96
12m
SuGaR
1.33
DNN-Spaltez
2,03
1.42
2.37
2.11
1.62
1.95
1
1.48
1.63
1.82
1:20
1.50
1.40
1,70
30m
Expllott
(Sartels [
0.66
0.93
0.54
0.41
1.06
1.14
0.85
1.29
1.53
0.79
0.82
1.58
0.45
0.66
0.55
0.88
11m
2DGS [8]
0.48
0.91
0.39
0.39
0.83
0.81
1.36
1.27
0.76
0.70
1,40
0.40
0.76
0.52
0.80
20m
6OF
0.74
2h
G82Mesh
0.79
0.78
0.59
0.46
0.68
20m
PGSR [X]
0.36
0.57
0.38
0.33
0.78
0.58
0.50
1.08
0.03
0.59
0.46
0.54
0.30
0.38
0.34
0.52
30m
GS- SullPlll [57]
0.51
0.56
0.46
0.39
0.82
0.67
0.85
1.37
1.25
0.73
0.54
1.39
0.35
0.58
0.42
0.75
22m
Rvv:3FF(Oirs
0:48
Reference Image
2DGS 18
GOF 5S
GS2Mcsh 45
PGSR 6
Eve3D (Ours)
Figure 4: Qualitative Comparison on the DTU Dataset. Visual comparison of 3D meshes recon- structed by our approach versus previous methods.
Implementation Details. Training Eve3D occurs in two phases: first, we pre-train vanilla 3DGS model using [3). which we use to render pseudo stereo views that feed into [44] to predict dense depth maps D* as priors. We use the pretrained model without fine-tuning, ensuring zero-shot generalization with no overlap between its training data and our evaluation datasets. We employ left-right consistency check with 3 3-pixel threshold to estimate confidence masks M We set Tooine to 7000, introduce depth prior supervision after the first 500 iterations, and apply local bundle adjustment from the beginning of training. We also built the Eve3D variant, reducing the total iterations from 30k to 5k and setting Tjoint to 1000. Following PGSR (6. we set Adn = 0.015, A, 100.0, and d 1.0. For our proposed losses, we use Aprior = 0.05, Xpult 0.05, and Asba = 0.15. These hyperparameters remain fixed across all datasets without tuning. Finally, we use K neighboring views for local bundle adjustment across all experiments.
4.2 Evaluation Against State-of-the-Art
Tanks and Temples. The Tanks and Temples dataset 24 collects scenes in a surround manner, comprising six diverse environments that include both indoor and outdoor scenarios with varying scales and lighting conditions. Tab. [ preseents our quantitative evaluation based on F1-score (the higher the better). Eve3D achieves superior performance with an average of 0.58, outperforming both implicit methods and other explicit approaches. Our method achieves the highest ranking in five scenes (Barn, Caterpillar, Courthouse, Meeting Room, and Truck) and ranks second in one scene (Ignatius). The most significant improvements appear in challenging scenarios: Eve3D achieves an Fl Score of 0.46 in Meetingroom (compared to 0.33 from PGSR) and 0.35 in Courthouse (compared to 0.28 from the best competitors). Despite these substantial quality improvements. Eve3D (trained for 30K iterations) maintains a reasonable 1.2-hour total training time (including 3DGS pretraining, stereo rendering, and depth prediction) significantly faster than implicit methods (>24h) while delivering superior reconstruction quality. Our Eve3D-fast variant, trained for only 5K iterations (20 minutes total time), still achieves second-best reconstruction quality with an average Fl-score of 0.56, setting an unprecedented trade-off between accuracy and speed. Fig. 3 shows Eve3D addresses standard 3DGS limitations in scenes with varied lighting and complex architecture, producing more accurate flat surfaces and detailed structures in both indoor and outdoor settings.
7

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Table Quantitative Comparisons of Novel View Synthesis on the Mip-NeRF360 Dataset.
indicate the absolute, second, and third bests, respectively.
Outdoor Scenes
Indoor Scenes
PSNR
SSIM |
LPIPS 4
PSNR
SSIM
LPIPS
NeRF 32
21.46
0.458
0.515
26.84
0.790
0.370
Deep Blending
21.54
0.364
26.40
0.261
Instant NGP
22.90
0.566
0.371
29.15
0.880
0.216
MERF
23.19
0.616
0.343
27.80
0.855
0.271
BakedSDI
22.47
0.585
0.349
27.06
0.836
0.258
MipNeRF360
24.47
0.283
31.72
0.917
0.180
SuGaR 16
0.629
29.43
0.225
3DGS 22
24.64
0.731
0.234
30.41
0.920
0.189
2DSS 18
24.34
0.717
0.246
30.40
0.916
0.195
GOF 655
24.76
0.742
0.225
30.80
0.928
0.167
PGSR [
24.76
0.752
0.203
30.36
0.934
0.147
Eve3D (Ours)
24.99
0.758
0.203
30.42
0.930
0.157
Reference Image
2DGS 18
GOF 55
PGSR
Eve3D (Ours)
Figure 5: Qualitative Comparison the Mip-NeRF360 Dataset. Visual comparison of 3D meshes
reconstructed reconstructed by our approach versus previous methods.
DTU. Tab. 2 shows our Chamfer Distance evaluation (lower better) on DTU Eve3D achieves the best average performance with score of 0.48, outperforming both the best implicit method (Neu- ralangelo at 0.61) and the previous best explicit approach (PGSR at 0.52). Our method demonstrates remarkable consistency, achieving the best performance in any scenes except one, where it is the second-best. Importantly, our method maintains an efficient 15-minute total training time (including all preprocessing steps), comparable vanilla 3DGS while delivering largely superior reconstruction quality. Qualitative comparisons Fig. show that while existing 3DGS methods produce good object meshes, they struggle with sparse viewpoints and inconsistent multi-view images. Our method enhances robustness in these difficult scenarios, yielding more complete, detailed meshes with better preserved fine structures.
Mip-NeRF360. For validating rendering quality, we evaluate on the Mip-NeRF360 dataset following 3DGS's standard protocol. Tab. shows results using standard metrics. Eve3D achieves excellent synthesis across outdoor and indoor scenes. For outdoor scenes, Eve3D delivers best performance all metries: PSNR (24.99), SSIM (0.758), and LPIPS (0.203), outperforming both NeRe-based approaches and recent 3DGS methods. indoor scenes, instead, our method achieves the second-highest SSIM (0.930) and LPIPS (0.157) scores, while maintaining competitive PSNR (30.42, third-bee best after MipNeRF360 and GOF). This demonstrates that our geometry-ave aware optimization also enhances rendering quality. Fig. further highlights this qualitatively.
4.3 Ablation Study
We ablate the key components Eve3D on the Tanks and Temples 24 dataset in Tab.
Single-view Prior Loss. Due to the lack of explicit geometric constraints, the baseline model struggles to reconstruct accurate surfaces relying solely on RGB supervision. When single-vies view depth prior is introduced to constrain the rendered depth and normals of 3D Gaussians, the surface reconstruction performance improves from 0.340 to 0.463. We also ablate the use of single-view prior loss in joint optimization setting, where the prior depth maps are treated as learnable parameters. This leads further improvement from 0.523 to 0.539. Please refer the supplementary material for experiments using different types of single-view priors derived from other vision models.

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w/ Joint Optimization Figure 6: Qualitative Mesh Comparison. Results without (w/o) and with (w/) joint optimization.
Reference
w/o Joint Optimization
Local Bundle Adjustment. Local bundle ad- justment applies explicit multi-view consistency constraints on the rendered depth and prior depth maps. Solely with local bundle adjust- ment, the performance is improved significantly from 0.340 to 0.523. In this configuration, prior depths guide the multi-view consistency but re- main non-learnable. Enabling joint optimization, which treats priors as learnable parameters D, yields further improvement from 0.523 to 0.539, demonstrating that allowing the model to refine priors through backpropagation enhances both prior quality and 3DGS reconstruction. When using both single-view prior loss and local bundle adjustment, jointly optimizing 3DGS and depth priors also has significant effects, improving the FI score from 0.553 0.581.
Table 4: Component Contribution. Evaluation of each module's effect on reconstruction quality.
Single-view Prior Loss
Evaluntion
Adjustment
Optimization
P1
R
Fl
0.297
0.418
0.340
0-41
0,519
0.463
0.437
0.523
0.467
0.483
0.594
0.523
0,504
0.605
0.539
0.553
0,553
0.631
0.581
Joint Optimization. We analyze the impact of each joint optimization component in Tab The local bundle adjustment pre-training plays an important role in initializing multi-view con- sistent priors; removing it leads to a performance drop from 0.581 to 0.574. Disabling the confi- dence mask update causes a slight drop to 0.578, highlighting the benefit of including more prior regions-wher validated by geometry bnecks-during joint optimization. Fig 6 compares results with and without joint optimization. Jointly optimizing Gaussians and priors enables reconstruction of fine details, while disabling it leads to over-smoothed surfaces due to excessive reliance on prior supervision. Furthermore, this joint strategy also especially effective for recovering geometry details that are very ambiguous from single-view visual clues, such as the dark areas in the Truck.
Table 5: Component Contribution Joint Opti- mization. Evaluation of each module's 's effect.
Methods
R1
Fl f
Optimization (Full)
0.631
0,581
w/o LBA Pre-training
0.549
0.618
0.574
w/o Confidence Mask Update
0.552
0.626
0.578
In Tab. 6 we compare the FoundationSterro 44 prior depths before and after joint optimization with Eve3D. We evaluate depth accuracy on the Tanks and Temples dataset using ground-truth depth maps provided by the RobustMVD benchmark. Among the four scenes available in RobustMVD, we use the three that overlap with our experimental setup: Barn, Courthouse, and Ignatius.
Since the camera poses estimated by COLMAP not aligned with the ground-truth poses poses-leading to inconsistencies depth scale- we perform mesh-to-mee to-meshh alignment between the reconstructed and ground-truth geometry obtain accurate scaling and alignment information. We then compute the relative depth error to quantify performance.
As shown in the comparison, in all evaluated scenes, the Eve3D rendering results consistently outperform the initial priors in depth accuracy. Furthermore, our proposed joint optimization strategy improves the accuracy of the initial depth priors.

---

Table Impacts Joint Optimization. We evaluate the accuracy of the learned depth priors before and after joint optimization using ground-truth depth maps from the Tanks and Temples dataset, provided by the Robust Multi-view Depth (RobustMVD) benchmark
36
Relative error rates are
reported to quantify the improvement in depth estimation.
Methods
Barn (%)
Courthouse (%)
Ignatius (%)
FourdationStereo
44
1.84
12.25
1.43
Optimized FoundationSterco
1.51
11.96
1.00
Eve3D Depth
1.48
11.79
0.80
Prior
Multi-view Consistency
Prior
Multi-view Consistency
w/o Joint Optimization
w/ Joint Optimization
Figure 7: Visualization of the multi-view consistency without and with the joint optimization. The consistency of current-view view depth priors is measured by computing the pixel-wise reprojection differences with the eight nearest neighbor views, followed by a consistency measurement using an exponential decay function, exp(-d), where d denotes the reprojection error. This weighting emphasizes geometrically consistent regions and suppresses unreliable estimates. Joint optimization significantly improves the multi-vies viet consistency.
Additionally, in Fig 7 we highlight another key property of the priors: multi-viww consistency. While initial priors provide reasonable but coarse estimates depth and normals, they often exhibit inconsistency across different viewpoints. This inconsistency introduces noise, which can degrade the quality supervision during Gaussian optimization. With our proposed joint optimization strategy, the multi-view consistency of priors is significantly improved, resulting in more stable and accurate supervision signals.
Conclusion
We presented Eve3D, novel framework for dense surface reconstruction based 3DGS. Our approach jointly optimizes both self-derived stereo depth priors and the 3DGS representation, estab- lishing mutually beneficial relationship in which each component improves the other. Our local bundle adjustment strategy ensures global consistency across view-overlapping frames, effectively compensating for the local supervision limitations inherent in 3DGS. Extensive experiments Tanks Temples, DTU, and Mip-NeRF360 demonstrate that Eve3D achieves state-of-the-ant performance in both surface reconstruction and novel view synthesis, while training in as little 15-20 minutes for our fast version, and ~ GPU hours for our base approach.
Limitations. Eve3D sets new state-of-the-art, yet with some trade-offs. Its primary constraint the reliance of a vision foundation model for stereo depth estimation. This choice motivated by the unpaired accuracy of the estimated depth priors compared to alternative monocular (48) or multi-view stereo [99 solutions, as discussed in the supplementary material. However, it requires rendering stereo images from the model itself an overhead that could be avoided if multi-view stereo foundation model could achieve comparable accuracy.
10

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Acknowledgments and Disclosure of Funding
This work is supported by Rawmantic AI, and the National Natural Science Foundation of China 62276016, 62372029.
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Appendix for "Eve3D: Elevating Vision Models for Enhanced 3D Surface Reconstruction via Gaussian Splatting"
Overview
This appendix contains supplementary material that supports and extends the findings presented in the main paper. We begin in Sec. A with detail descriptions of our experimental setup. Sec. B provides further analysis, including ablation studies to better understand different components of our approach. Additional qualitative results of 3D reconstructions produced by Eve3D are presented in reflect the broader impact of our methodology.
Sec. C Finally, in Sec.
A
Details of Experimental Setting
A.1 Datasets
DTU. The DTU dataset provides ground-trute truth point clouds for evaluating object- reconstruction quality. Following prior works [18 [5 6 we use 15 scans (24, 37, 40, 55, 63, 65, 69, 83, 94, 102, 106, 110, 114, 118, and 122) to assess surface reconstruction performance. In our experiments. all images from each scan are used, downsampled to half resolution for training.
Tanks and Temples. The Tanks and Temples dataset includes ground-truth points for evaluating surface reconstruction in both indoor and outdoor scenes, In line with previous studies [1] [5 6].
we conduct experiments on six scenes: Barn, Caterpillar, Courthouse, Ignatius, Meetingroom, Truck. For each scene, we use all available images, downsampled to half resolution for training.
Mip-NeRF360 Since Mip-NeRF36 NBRR660 does provide ground-trute -ttth points for surface reconstruction evaluation, we instead use to evaluate novel view synthesis performance. We adopt the standard train/test splits from prior works [IS 55 []. For outdoor scenes (bicycle, flowers, garden, stump, treehill), images are downsampled to quarter resolution. For indoor scenes (bonsai, counter, kitchen, room), images are downsampled half resolution, consistent with previous studies [18] 55 6]. For
mesh reconstruction visualizations, we train models using only the training split images.
A.2 Implementations
Hyperparameters. Our base model adopts the plane depth definitions [6] to render depth. We constrain the shortest axis scale of Gaussians zero make Gaussians close planes. We adopt a depth-normal consistency loss [1, 55 6 to encourage the consistent representations of rendered depth and normal vectors. The learnable prior depth maps initialized using predictions from depth estimation model and optimized with learning rate of (1 x 10 10-+). For depth map initialization, we sample 500,000 points for DTU scans and 1,000,000 points for both Tanks and Temples scenes and the Mip-NeRF360 dataset.
Eve3D. We train Eve3D for total of 30,000 iterations. Prior depth supervision is introduced starting from iteration 500. We set the Tjoine to 7000. The shortest axis scale loss is applied from the beginning of training. The depth-normal consistency is activated starting at iteration 7000. The densification process for 3D Gaussians begins at iteration 500 and ends at iteration 15,000.
Eve3D-fast. We train Eve3D-fast for total of 5,000 iterations. Prior depth supervision is introduced starting from iteration 500. We set Tyoint = 1000. The shortest axis scale loss is applied from the beginning of training. The depth-normal consistency is enabled from iteration 1000. The densification of 3D Gaussians begins at the 500 iteration and concludes at iteration 4000.
Mesh Extraction. We render depth maps from the 3D Gaussians and apply Truncated Signed Distance Function (TSDF) fusion extract surface meshes. For scenes captured with front-facing cameras (DTU), we use unbounded mesh extraction and set the voxel size to 0.002. For scenes captured by surround-view cameras (e.g., Tanks and Temples, Mip-NeRF360), we use bounded mesh extraction, where the voxel size is set to the maximum scene extent divided by 2048. For indoor scenes, scene bounds are estimated from camera trajectories, while for outdoor scenes, they are estimated from the reconstructed point clouds.
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Table 7: Direct Comparisons between Eve3D and GS2Mesh. Methods are trained with mini- splatting2 to render stereo views and FourdationStereo to predict depth maps.
Methods
Barn
Caterpillar
Courthouse
Ignatius
Meetingroom
Truck
Mean
Time
GS2Mesh 445]
0.51
0.27
0.08
0.61
0.19
0.41
0.35
12 m
Eve3D-fast (Ours)
0.69
0.44
0.34
0.82
0.41
0.62
0.56
20 m
Eve3D (Ours)
0.70
0.48
0.35
0.83
0.46
0.66
0.58
1.2
Table 8: Comparisons to PGSR with Depth Priors. We train PGSR with the FoundationStereo initialization and supervision, which is the same to the supervision used in Eve3D. The difference is that Eve3D uses the Prior-involved bundle adjustment with joint optimization, while PGSR uses multi- view consistency between neighbor-view rendering results to maintain the multi-view consistency. With the same depth prior, Eve3D shows significantly better convergence than PGSR.
Total
PGSR + FourdationSterco
Eve3D (Ours)
Iterations
Precision
Recall f
F1 Score
Precision
Recall t
Fl Score
3k
0.494
0.531
0.485
0.500
0.570
0,525
0.492
0.574
0.519
0,532
0.600
0.555
10k
0.505
0.586
0.532
0.546
0,611
0,568
30k
0.552
0.609
0.571
0.553
0.631
0.581
Overlapping Score. Following [51] for a reference view V,, we compute the overlapping score sii,j) = Σx (X)) for its neighboring view V,, and X is a 3D point which is observed by both views and Vj. In detail, d,y (X) (180/T) arccos( X) (t, X)) the baseline angle and represents the camera center. 17()) is piece-wise Gaussian function that favors a certain baseline angle 0g:
cxp
(0-0g)² 0g) 2 (0 0g
if 0o
(0)
(16)
exp
20
if 0 0p
where lo. do and are hyper-parameters and are set to 5, 1, and 10 respectively.
B
Additional Analysis
B.1 Direct Comparisons with Improved GS2Mesh
Eve3D leverages the rendering capabilities of 3DGS generate stereo pairs and infer depth pri- ors mis-iamilar in spirit to GS2Mesh [45]. However, while GS2Mesh directly uses stereo depth maps to reconstruct the meshes, apporoach treats stereo depth maps priors, which are then jointly optimized along with the 3D Gaussian via our proposed framework.
Although the original GS2Mesh significantly underperforms compared to Eve3D in terms of recon- struction accuracy (see Tab. 2 in the main paper), the reader might argue that the discrepancy could be due to the of different stereo backbone- Debbonn-DLLRRR [59). Therefore, to fully assess the superiority of our methodology over the direct fusion of stereo priors, we re-implement GS2Mesh using the same settings Eve3D.
Accordingly, we provide new comparison between GS2Mesh and Eve3D in Tab. On the Tanks and Temples dataset. In this experiment, both methods use Mini-Splatting [3] to render pseudo stereo views and FoundationStereo priors, GS2Mesh struggles to reconstruct accurate surfaces- -articccl-aat in complex scenes like Meetingroom and Courthouse. contrast, our Eve3D-fast, with only eight minutes of additional optimization, achieves significantly better reconstruction quality.
to estimate depth maps. Even with such high-quality
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Table Ablation Study. Impact of vision model choice.
Prior Source
Eve3D (Ours)
Precision 1
Recall
F1 Score
FoundationSteren
0.431
0.519
0.463
FoundationStereo
44
0.553
0.631
0.581
Stereo Anywhere
3
X
0,410
0.470
0,431
Stereo Anywhere
0.533
0.600
0.555
MVSAnywhere
0,430
0.517
0.462
MVSAnywhere
0,506
0.578
0,532
MVSFormer
0.450
0.548
0.483
MVSFormer
0.493
0.598
0.528
OMNI-DC
64
X
0.298
0.390
0.330
OMNI-DC
64
0,448
0.540
0,479
Methods with Foundation Storeo
8364
0:33
6559
PGSR
Eve3D
6.415
0.493
3k:5k
10k
30k
Irerations
Figure 8: Comparisons of PGSR and Eve3D with FoundationSterco priors convergence speed.
B.2
Comparisons to PGSR with Depth Prior
PGSR [6] sets strong baseline for surface reconstruction with the proposed multi-view consistency based on rendered results. Compared to PGSR, Eve3D uses the prior-depth involved bundle ad- justment enhance the multi-view consistency, incorporating more than neighbor view in one training loop. Moreover, Eve3D leverages bundle adjustment not only to refine the 3D Gaussians but also optimize the depth priors themselves. When the initial priors are reasonably accurate at a coarse level, they can be quickly refined into multi-view consistent priors through local bundle adjustment. This leads better convergence behavior compared enforcing multi-view constraints directly on rendering outputs, done in PGSR. As shown in Table8 and Figure&] when using the same FoundationStereo priors, Eve3D achieves significantly faster and more stable convergence than PGSR.
Table 10: Ablation Study. Impacts of baseline length.
Baseline Length
& camera extent
7 % camera extent
10 camera extent
Fl soore f
0.580
0.581
0.581
B.3 Additional Ablation Study on Method Components
Prior Depth Types. We evaluate the generalizability of our method across alternative sources for depth priors, replacing those obtained from FoundationStereo applied to rendered stereo images with different approaches: i) using the Stereo Anywhere model 33; ii) using depth maps from multi-view stereo Anyhhere [19] and MVSFormer 4 or iii) a depth completion network (OMNI-DC (64]) applied to sparse depth points extracted from COLMAP, All these methods predict depth maps

---

Table 11: Ablation study. Impact of the neighbors in local bundle adjustment.
Number of Neighbors
Precision f
Recall t
F1 Score f
Training Time
0.544
0.625
0.573
50
0,551
0.627
0.578
1 h
0.553
0.631
0.581
1.2
8
0.534
0.631
0,582
1,5 h
the correct metric scale, although through different working principles: stereo models estimate disparity maps from stereo images rendered using intrinsics and extrinsics at the same scale as the pretrained 3DGS, then triangulate depth using the known focal length and baseline; multi view stereo methods exploit the same camera poses used to optimize 3DGS, thus predicting depth at consistent scale; depth completion models densify sparse COLMAP points used to initialize 3DGS, maintaining their metric scale. Despite variations in depth accuracy across these sources, our joint optimization consistently improves reconstruction performance (Table 9). demonstrating robustness to different depth initializations and strong generalization. We emphasize that all foundation models used in our experiments are applied zero-shot without fine-tuning.
We also highlight how, at the current stage, rendering stereo images to extract priors through FoundationStereo [44] represents the optimal choice; nonetheless, don't exclude that future advances in multi-view stereo or depth completion may lead to stronger models, thus making Eve3D no longer require rendering stereo images get priors. Confirming this hypothesis in future research would allow for further improve Eve3D performance as can be seamlessly integrated even with future, more advanced networks.
Virtual Camera Baseline Length. For stereo pair generation, we set the baseline length to 7% of the scene radius across all experiments. To assess the sensitivity of our method to this choice, evaluate different baseline lengths in Table 10 The results demonstrate that our method is robust to baseline selection, with F1 scores remaining consistent (0.580-0.581) across baseline lengths ranging from 3% to 10% of the camera extent. This robustness stems from our joint optimization and local bundle adjustment, which enforce multi-view consistency constraints that naturally compensate for variations in initial stereo depth estimates.
Number of Neighbors in Local Bundle Adjustment. We study the impact of the number of neighboring views used in local bundle adjustment in Table the number of neighbors increases, the training time also grows due to the additional computation. At the same time, the inclusion of more diverse viewing angles each optimization step enhances the geometric accuracy of both the 3DGS representation and the optimized depth priors. However, beyond certain point, the benefit of adding more neighbors saturates. This is because additional views with weaker co-visibility relationships contribute limited new information, resulting in diminishing returns in geometric improvement.
B.4 Training Time Breakdown
All training times reported in the main paper include the complete pipeline: 3DGS pretraining, stereo pair rendering, FoundationStereo predictions, and final 3DGS training. We provide a detailed breakdown of preprocessing and training times for transparency.
Tanks and Temples: On average, it takes 4 minutes to pretrain 3DGS with Mini- Mini-Splatting (13), minutes for stereo pair rendering and FoundationStereo depth predictions, and 60 minutes for final Eve3D training minutes for Eve3IDfast). The total time is therefore 1.2 hours for Eve3l and 20 minutes for Eve3D-fast.
DTU: On average, it takes 3 minutes pretrain 3DGS with Mini-Splatting, 4 minutes for stereo pair rendering and FoundationStereo predictions, and 8 minutes for final training. The total time is 15 minutes.
C Additional Visualization Results
We provide additional qualitative results of Eve3D Fig. 9 TO and which illustrate the surface reconstructions the Tanks and Temples, DTU, and F360 datasets, respectively.
25

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Figure Qualitative Visualizations on the Tanks and Temples Dataset.
Figure 10: Qualitative Visualizations on the DTU Dataset.
D Broader Impact Statement
Eve3D sets a new state-of-the-arl in 3D surface reconstruction, achieving unprecedented accuracy with very low time and hardware budget.
On the one hand, Eve3D has the potential accelerate progress across several high-level applicative domains, including augmented/virtual reality, robotics, autonomous navigation/interattoo with the environment, and 3D content creation. The accuracy-speed trade-off achieved by Eve3D could repre- sent a strong opportunity to democratize access to high-qualit, 3D modeling, by significantly lowering the entry barriers for researchers, educators, or any independent developers. Furthermore, faster convergence speed also translates into a reduced carbon footprint associated to 3D reconstruction.
On the other hand, the possibility of producing higher-quality 3DD models also comes with ethical considerations. These latter could be misused for applications such as surveillance or other privacy- infringement purposes, However, we argue Eve3D is not designed handle dynamic objects/subjects during the reconstruction process, thus making it unsuited for processing casually collected videos where subjects may appear without their explicit consent.
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Figure 11: Qualitative Visualizations on the Mip-NeRF360 Dataset.
27


Reviews:
[{"review_id": "4sRGMfejfk", "reviewer": "Reviewer_p1BA", "summary": "This paper proposes Eve3D, a dense 3D reconstruction approach based on 3D Gaussian Splatting. Given that the depth priors obtained from pre-trained vision models are imperfect, a joint optimization mechanism is proposed in Eve3D, where the priors first improve the quality of 3D GS, which in turn refine the priors. This iterative process gradually improves the 3D reconstruction quality. Moreover, a bundle adjustment based optimization step is also introduced to overcome the local supervision of 3DGS. Experimental results on T&T, DTU and Mip-NeRF360 datasets demonstrate the effectiveness of the proposed method.", "questions": "Please refer to the weaknesses.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths\n1. Extensive experiments have been conducted on three benchmarks, and the proposed method achieves better performance compared to existing works.\n2. Detailed ablation studies have been provided as well as the corresponding analysis.\n\nWeaknesses\n1. The presentation of the method is not very clear. There are different stages involved in the whole pipeline, which includes optimizing over 3D Gaussians and the learnable depth. However, it is not clear which losses are used at different stages, does the loss function in Eq.(10) only responsible for refining the learnable depth prior or both? After switching to optimizing the learnable depth prior, will it still switch back to optimize 3DGS? The optimizing targets are different before and after T_joint  to the text, but is not expressed in Eq (13)? In addition, Figure 2 is not clear about the alternative optimization.\n2. The proposed method involves complicate training process with many loss terms. Are the weights for different losses set to one according to Eq.(13)?  Are these weights values applicable to different datasets or hyperparameter tuning is required? How to decide the T_joint and how does it affect?\n3. The introduction of depth-normal consistency loss and the scale loss should be self-contained by either including references or descriptions.\n4. Missing references.\nVCR-GauS: View Consistent Depth-Normal Regularizer for Gaussian Surface Reconstruction. Neurips 2024.", "was_revised": true, "final_justification": "Confusion over the method is resolved in the rebuttal, authors are encouraged to include the explanation to improve the presentation for final version if the paper gets accepted.", "rebuttal": "### **Responses to Reviewer p1BA** ###\n\nWe thank the reviewers for their insightful feedback. Below we address the questions and concerns raised.\n\n---\n\n>**Q1: The presentation of the method is not very clear. There are different stages involved in the whole pipeline, which includes optimizing over 3D Gaussians and the learnable depth. However, it is not clear which losses are used at different stages, does the loss function in Eq.(10) only responsible for refining the learnable depth prior or both? After switching to optimizing the learnable depth prior, will it still switch back to optimize 3DGS? The optimizing targets are different before and after $T_{joint}$ to the text, but is not expressed in Eq (13)? In addition, Figure 2 is not clear about the alternative optimization.**\n\nWe acknowledge that our description of the optimization stages could be clearer, and we will improve this presentation in the revision. To address the reviewer's specific concerns: \n    \n**Regarding the loss functions at different stages:** Eq. (13) already reflects the different optimization targets. Before $T_{joint}$, rendered depth $D$ is supervised by fixed stereo depth priors $D^* $ using $||D^* - D||$, optimizing only 3DGS parameters. After $T_{joint}$, we switch to $||\\hat{D} - D||$ (where $\\hat{D}$ is a learnable depth map initialized with $D^*$), enabling joint optimization of both $D$ and $\\hat{D}$ through backpropagation. The local bundle adjustment loss Eq. (10) is applied on prior depths to provide multi-view consistent prior initialization before $T_{joint}$, and then optimizes both rendered depths and prior depths jointly after $T_{joint}$.\n\n**Regarding the optimization schedule:** After switching to $T_{joint}$, there is no alternating optimization of depth priors and 3DGS parameters: we continue with joint optimization of both for all the remaining iterations, where both single-view prior loss Eq. (13) and local bundle adjustment loss Eq. (10) operate on both 3DGS and prior depths simultaneously after $T_{joint}$.\n\n**Regarding Figure 2 clarity:** We recognize that Figure 2 does not clearly show the temporal aspects of our method. The bidirectional arrows between \"Learnable Depth Priors\" and \"3DGS\" represent the joint optimization phase after $T_{joint}$, but we failed to clearly indicate that before $T_{joint}$, supervision occurs only in a single direction (from fixed priors to 3DGS). We will revise Figure 2 to explicitly show these two phases and add timeline indicators to eliminate any remaining ambiguity.\n\n---\n\n>**Q2: The proposed method involves complicate training process with many loss terms. Are the weights for different losses set to one according to Eq.(13)? Are these weights values applicable to different datasets or hyperparameter tuning is required? How to decide the T_joint and how does it affect?**\n\nThanks for pointing this out. We use the same set of weight values in all datasets without further tuning required. In our final loss, the weight of color loss $L_{c}$ is one. The depth-normal consistency loss $L_{dn}$ and scale loss $L_{s}$ are from PGSR implementations. We follow PGSR to set the loss weights $\\lambda_{dn}=0.015, \\lambda_{dn}=100.0$. For single-view prior loss, pull loss and local bundle adjustment loss, we set the weight values $\\lambda_{prior}=0.05, \\lambda_{pull}=0.05, \\lambda_{lba}=0.15$. The introduction of $T_{joint}$ is due to 3D Gaussians are initialized from point cloud and need a few iterations of optimization to render reasonable contents. We set the default value of $T_{joint} = 7k$, primarily to align with the iteration at which the depth-normal consistency loss begins, following the practice of 2DGS, GOF, and PGSR. The choice of $T_{joint}$ is robust because, with local bundle adjustment providing multi-view consistency supervision, it is not necessary for 3DGS to be perfectly optimized. For example, the Eve3D-fast variant reduces the total number of iterations from 30k to 5k, sets $T_{joint} = 1k$, and still achieves good performance. We will add more details and discussions of the hyperparameter in the revised implementation details.\n\n---\n\n>**Q3: The introduction of depth-normal consistency loss and the scale loss should be self-contained by either including references or descriptions.**\n\nThank you for this suggestion. The depth-normal consistency is from PGSR implementation, and similar implementations are also used in 2DGS, GOF. The scale loss is from PGSR. We will add detailed descriptions and references of the depth-normal consistency loss and scale loss to make the section more self-contained.\n\n---\n\n>**Q4: Missing references. VCR-GauS: View Consistent Depth-Normal Regularizer for Gaussian Surface Reconstruction. Neurips 2024.**\n\nThanks for pointing this out. We will include the VCR-GauS reference in our revision as it is indeed to our depth-normal consistency formulation.\n\n---"}, {"review_id": "FqHyvlM61J", "reviewer": "Reviewer_NuAQ", "summary": "The authors propose Eve3D, a 3D reconstruction method which jointly optimizes 3DGS and the depth priors supervised by 3DGS. It renders stereo pairs with a pretrained 3DGS and acquires initial depth maps from FoundationStereo. The depth maps are subsequently refined with local bundle adjustment in high-quality regions and guided by 3DGS depth in low-quality regions to improve multi-view consistency, which then serve as depth and normal supervision for 3DGS. The authors test Eve3D on DTU, Tanks and Temples, and Mip-NeRF 360 datasets, where it outperforms its counterparts in terms of reconstruction quality on most of the scenes. Substantial ablations are performed to validate the effectiveness of joint optimization and local bundle adjustment.", "questions": "1. Is the local bundle adjustment applied to the learnable depth priors only, or to 3DGS depth as well? Section 3.2 only mentions the depth priors, while supp. material mentions that both are optimized (L92-93).\n2. What are the specific settings for the ablations in Table 4, middle group (without the single-view prior loss)? Is the local bundle adjustment in this case only applied to 3DGS? Why would joint optimization make a difference here if the depth priors are unused in supervision?\n3. As discussed above, please clarify whether the training time reported takes pretraining and depth prior computation into account. Adding also a breakdown of preprocessing time (3DGS pretraining, stereo pairs rendering, FoundationStereo prediction, triangulation) would be more helpful.\n4. If possible, please add some discussions on the robustness of Eve3D in sparse-view settings (or when pretrained 3DGS/depth priors have poor quality), including visualizations and failure cases, if any.\n5. What is the number of neighboring views used in local bundle adjustment for the main results reported?\n6. For Fig. 8 in supp. material: it would be more informative to present a metrics vs. time plot with more data points till convergence, given that the number of neighbors used affects the training speed. Better if results of PGSR using more than one neighbor view per iteration are also presented.\n7. Is the confidence mask (L183) used solely in $\\mathcal{L}_{pull}$ (Eq. 12)? L280-284 hints that $\\hat{\\boldsymbol{M}}^c$ controls prior supervision regions, but this is not reflected in Eq. 13 and Eq. 14. Also, what is $\\Gamma$ in Eq. 14?\n8. L214, 216: Should be $T_{joint}$ instead of $T_{switch}$?", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- The idea of optimizing high-quality depth priors jointly with 3DGS to handle multi-view inconsistency and compensate for inaccuracies in the priors is straightforward but solid. The reported reconstruction performance on DTU and Tanks & Temples is also impressive, where the method achieves very competitive results even in equal-time comparisons (the Eve3D-fast variant).\n- Comprehensive ablations are conducted to study the effect of different depth priors, local bundle adjustment, and joint optimization. The results, in general, demonstrate the benefits of refining depth priors with local bundle adjustment and optimizing jointly with 3DGS, compared to previous works.\n\nWeaknesses:\n\nI do not observe any major weakness. Some minor issues include:\n- As Eve3D’s performance depends on the quality of initial depth priors to some degree (as discussed in supplementary material), which further depend on the stereo pairs rendered by pretrained 3DGS, it would be helpful to show and discuss how robustly Eve3D performs with sparse input views. In cases where the rendered stereo pairs are of low quality, it might be interesting to compare with priors from monocular depth estimation (or feeding identical views to the stereo models).\n- Pretraining 3DGS to render the stereo pairs and attaining the depth maps from FoundationStereo incur extra (though potentially minor) overhead on the pipeline. Currently there is little information on the time taken in this stage, and whether it is included in the reported Eve3D training time is unclear.\n- Some clarity-related issues, please refer to the questions part.", "was_revised": true, "final_justification": "I retain my initial rating as my concerns on clarity, robustness, and computational efficiency have been addressed after reading the rebuttals and other reviews.", "rebuttal": "### **Responses to Reviewer NuAQ** ###\n\nWe thank the reviewers for their insightful feedback. Below we address the questions and concerns raised.\n\n---\n\n>**Minor issues**\n\n>- As Eve3D’s performance depends on the quality of initial depth priors to some degree (as discussed in supplementary material), which further depend on the stereo pairs rendered by pretrained 3DGS, it would be helpful to show and discuss how robustly Eve3D performs with sparse input views. In cases where the rendered stereo pairs are of low quality, it might be interesting to compare with priors from monocular depth estimation (or feeding identical views to the stereo models).\n\n\n>- Pretraining 3DGS to render the stereo pairs and attaining the depth maps from FoundationStereo incur extra (though potentially minor) overhead on the pipeline. Currently there is little information on the time taken in this stage, and whether it is included in the reported Eve3D training time is unclear.\n\nThanks for these observations. We are going to answer both points according to the questions raised below.\n\n---\n\n>**Q1: Is the local bundle adjustment applied to the learnable depth priors only, or to 3DGS depth as well? Section 3.2 only mentions the depth priors, while supp. material mentions that both are optimized (L92-93).**\n\n\nBoth learnable depth priors and 3DGS depth are optimized by local bundle adjustment. Before $T_{joint}$, local bundle adjustment is applied only between depth priors to provide a multi-view consistent initialization (main paper, L175-179). After $T_{joint}$, local bundle adjustment is applied between depth priors and 3DGS depth to encourage multi-view consistency for both priors and 3DGS itself (main paper, L139-141). We will make the descriptions clearer in the revision.\n\n---\n\n>**Q2: What are the specific settings for the ablations in Table 4, middle group (without the single-view prior loss)? Is the local bundle adjustment in this case only applied to 3DGS? Why would joint optimization make a difference here if the depth priors are unused in supervision?**\n\nThe depth priors are used in both the Local Bundle Adjustment and Joint Optimization columns. The difference is that 'Joint Optimization' treats the prior depths as learnable parameters, thus the priors and 3DGS depths are jointly optimized. 'Local Bundle Adjustment' uses prior depths as source views and 3DGS depths as reference views, but this does not necessarily mean that the priors are treated as learnable parameters and jointly optimized. The ablation study with and without 'Joint Optimization' demonstrates the effectiveness of setting priors as learnable parameters. We will clarify these settings in the revision.\n\n---\n\n\n>**Q3: As discussed above, please clarify whether the training time reported takes pretraining and depth prior computation into account. Adding also a breakdown of preprocessing time (3DGS pretraining, stereo pairs rendering, FoundationStereo prediction, triangulation) would be more helpful.**\n\nWe confirm that the reported training time includes all steps in the pipeline: 3DGS pretraining, stereo pair rendering, FoundationStereo predictions, and the final 3DGS training. For Tanks and Temples, it takes an average of 4 minutes to pretrain 3DGS with Mini-Splatting2 and 8 minutes for stereo pair rendering and stereo depth predictions. The final training time for Eve3D-fast is 8 minutes (for a total of 20 minutes), and for Eve3D is 60 minutes (for a total of 1.2 hours). For DTU datasets, it takes an average of 3 minutes to pretrain 3DGS with Mini-Splatting2, 4 minutes for stereo pair rendering and stereo depth predictions, and the final training time takes 8 minutes (for a total of 15 minutes). We will include a detailed timing breakdown of each preprocessing step in the appendix to provide better transparency.\n\n---\n\n>**Q4: If possible, please add some discussions on the robustness of Eve3D in sparse-view settings (or when pretrained 3DGS/depth priors have poor quality), including visualizations and failure cases, if any.**\n\n\nAlthough sparse-view reconstruction is not Eve3D’s primary target, we address the reviewer’s interest in robustness under challenging conditions by providing this analysis. In particular, we evaluate the robustness of Eve3D on the sparse 3-view DTU datasets. The comparison results are shown in the following table. We use the same hyperparameter setting as the dense-view DTU reconstruction. With only 3-view inputs, Eve3D still significantly improves the reconstruction quality from 1.82 mm to 1.08 mm. Eve3D also achieves comparable performances to FatesGS and Sparse2DGS (outperforming the latter), which are specifically designed for sparse-view surface reconstruction, whereas our method is not. The FoundationStereo can utilize robust monocular information from DepthAnythingV2, and we use the original RGB images as the left image of the stereo pair when performing stereo matching, which reduces the impact of poor 3DGS rendering quality on depth estimation. In the subsequent joint optimization, both prior depth and 3DGS depth are constrained by multi-view consistency. The monocular information from clean left-view images and multi-view consistency constraints improves the robustness of Eve3D to sparse-view cases.\n\n\n**Table: Reconstruction quality on the sparse 3-view DTU setting**\n\n| Methods             | our base w/o Eve3D | Eve3D | 3DGS | 2DGS | FatesGS | Sparse2DGS |\n|---------------------|--------------------|--------|------|------|---------|-------------|\n| **CD (mm) ↓**       | 1.82               | 1.08   | 3.54 | 1.69 | 0.92    | 1.13        |\n\n\n---\n\n\n>**Q5: What is the number of neighboring views used in local bundle adjustment for the main results reported?**\n\nThe main results are reported using 4 neighboring views, we will clarify this in the text.\n\n---\n\n>**Q6: For Fig. 8 in supp. material: it would be more informative to present a metrics vs. time plot with more data points till convergence, given that the number of neighbors used affects the training speed. Better if results of PGSR using more than one neighbor view per iteration are also presented.**\n\n\nThanks for the valuable suggestion. Unfortunately, we cannot provide a new figure during the rebuttal stage, but we will add the requested figure to the appendix in the revised version of the paper. For example, Eve3D needs 8 minutes and PGSR needs 7 minutes for training 5k iterations, and Eve3D needs 60 minutes and PGSR needs 45 minutes for training 30k iterations. For the convergence, both methods achieve the performance close to the final 30K iteration after 20k iterations. We will add more data points with running time to illustrate the convergence conditions from 10k to 30k iterations in Fig. 8. \n\n\nOur method jointly optimizes the depth priors and the 3DGS representation, where the prior depths are independent parameters. As a result, incorporating multiple neighboring views per iteration leads to more effective optimization. While PGSR imposes consistency between depths rendered from the shared 3DGS representation. Including more views in PGSR would require additional forward rendering passes per iteration, introducing extra computational overhead. Moreover, since all views share the same 3DGS geometry, the benefit of adding more views for regularization is limited compared to our approach.\n\n**Table: PGSR with more than one neighbor views**\n\n| Method              | F1 Score ↑ | Time |\n|--------------------|--------|------|\n| PGSR               | 0.516  | 45m  |\n| PGSR (4 neighbors) | 0.519  | 2h   |\n\n\n\n---\n\n>**Q7: Is the confidence mask (L183) used solely in $\\mathcal{L}_{pull}$ (Eq. 12)? L280-284 hints that $\\hat{M}^C$ controls prior supervision regions, but this is not reflected in Eq. 13 and Eq. 14. Also, what is $\\Gamma$ in Eq. 14?**\n\nThank you for pointing this out. The confidence mask introduced in Line 183 is indeed also applied to Eq.13 and Eq.14, where both losses are computed over confident regions. We will revise the paper to clearly indicate that the mask is used in Eq.13 and 14. $\\Gamma(A,B)$ is defined as $(1-A^TB)$ -- i.e., one minus the dot product between pixel-wise vectors in normals maps A and B.\n\n---\n\n>**Q8: L214, 216: Should be $T_{joint}$ instead of $T_{switch}$ ?**\n\n\nThis is indeed a notation error — it should be $T_{\\text{joint}}$ throughout. We thank the reviewer for pointing this out and will fix this inconsistency in the revised version.\n\n---"}, {"review_id": "IVqX31NA7m", "reviewer": "Reviewer_Bwjv", "summary": "This paper presents a 3DGS framework for surface reconstruction with joint optimization of depth priors. Specifically, a virtual camera is utilized to perform a standard binocular stereo matching in a multi-view setting. Besides, a prior-involved local bundle adjustment is proposed to further eliminate the noise in depth prediction.", "questions": "- How would the virtual camera affect the final results? Are the final results sensitive to the different choice of virtual cameras? An ablation study of different virtual cameras should be conducted. \n\n- The generalizability of this method should be well studied, as depth estimation may fail to predict the right depth scale. Are the results sensitive to the initialization of depth estimation?\n\n- Why did you choose depth priors other than normal or normal & depth?", "limitations": "Yes.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n- The paper introduces a novel approach that jointly optimizes pre-trained vision model priors and the 3D Gaussian Splatting (3DGS) backbone. This creates a mutually reinforcing cycle where priors enhance 3DGS quality, which in turn refines the priors, leading to significantly improved 3D reconstruction accuracy. The framework addresses the limitations of existing methods that rely on static priors.\n\n- By introducing a local bundle adjustment strategy, the method overcomes the highly local supervision limitations of standard 3DGS pipelines. This approach enforces global consistency across co-visible frames during optimization, ensuring more accurate surface reconstruction and addressing challenges like textureless regions and repetitive patterns. The quantitative results on benchmarks like Tanks & Temples and DTU validate its effectiveness.\n\n- Eve3D achieves superior results in surface reconstruction and novel view synthesis while maintaining fast convergence. \n \nWeaknesses\n \n- The framework relies on a vision foundation model for stereo depth estimation, which introduces an overhead of rendering stereo images from the model. While this improves accuracy, it limits the method’s independence and could be problematic in scenarios where such models are not available or efficient. The paper acknowledges this as a limitation but does not provide a robust alternative.\n\n- Although the paper mentions that model-generated priors contain noise, the analysis of how different levels of prior noise affect reconstruction quality is insufficient. A more detailed ablation study on the impact of prior accuracy across various scenes would strengthen the understanding of the method’s robustness in noisy conditions.\n\n- The experiments primarily focus on standard benchmarks, but the method’s performance in extreme scenarios (e.g., highly dynamic environments, low-light conditions, or extremely large-scale scenes) remains unaddressed. The paper does not provide evidence of its effectiveness beyond the tested datasets, limiting confidence in its broader applicability.", "was_revised": true, "final_justification": "Most of my concerns are addressed. The impact of different prior depths with Eve3D could be included in the supplementary materials.", "rebuttal": "### **Responses to Reviewer Bwjv** ###\n\nWe thank the reviewers for their insightful feedback. Below we address the questions and concerns raised.\n\n---\n\n>**Q1: The framework relies on a vision foundation model for stereo depth estimation, which introduces an overhead of rendering stereo images from the model. While this improves accuracy, it limits the method’s independence and could be problematic in scenarios where such models are not available or efficient. The paper acknowledges this as a limitation but does not provide a robust alternative.**\n\n\nOur method aims to explore a general and effective way to integrate rapidly advancing geometric models into the 3D reconstruction pipeline—a direction we believe is of high value for the field. While this involves using external depth estimation models, it is a common and effective practice in many state-of-the-art computer vision tasks and enables better accuracy and convergence. Crucially, our framework is not tied to any particular depth model. As demonstrated in Appendix Table 9, it is compatible with various types of priors, and consistently improves reconstruction quality. This robustness stems from that the depth prior is parameterized and optimized during training, as well as a multi-view geometric check during optimization, allowing our system to correct or adapt to imperfect inputs. This enables the method to maintain a high-quality lower bound, even in challenging scenarios where the prior may be noisy or partially failing.\n\nIn terms of efficiency, we emphasize that incorporating the stereo-based depth prior does not introduce additional overhead beyond our reported runtime. All timings, including stereo rendering, depth supervision, and 3DGS optimization, are already included in our measurements. The stereo pairs rendering is only needed once at the start of training. Moreover, the use of a depth prior not only improves final reconstruction quality but also accelerates convergence, providing a favorable trade-off between speed and performance (see the “-fast” variant). \n\nFor extremely difficult cases where both the prior network and 3DGS may struggle, we see future potential in integrating sparse LiDAR measurements to build more reliable priors. We plan to explore this direction further as part of future work.\n\n---\n\n\n>**Q2: Although the paper mentions that model-generated priors contain noise, the analysis of how different levels of prior noise affect reconstruction quality is insufficient. A more detailed ablation study on the impact of prior accuracy across various scenes would strengthen the understanding of the method’s robustness in noisy conditions.**\n\nThanks for the feedback. Since our method uses multi-view consistency to filter out noise in the prior, it is inherently robust to prior noise. The performance differences between different priors mainly arise because more accurate priors preserve larger valid regions after the multi-view geometric check, which in turn leads to better optimization of 3DGS. We analyze how prior quality affects final performance in Appendix Table 9. Additionally, in the following table, we quantify the accuracy of the prior depths to provide a clearer understanding of our method’s robustness to prior noise. These new results will be included in the revised version as part of Appendix Table 9.\n\n\n\n**Table: Impact of different prior depths with Eve3D. Relative error rates are reported to quantify the accuracy of prior depths.**\n\n| Prior Source       | Prior Error Rate (%) | Precision ↑ | Recall ↑ | F1 Score ↑ |\n|--------------------|--------------------|-------------|----------|-------------|\n| FoundationStereo   | 5.17               | 0.553       | 0.631    | 0.581       |\n| Stereo Anywhere    | 6.81               | 0.533       | 0.600    | 0.555       |\n| MVSAnywhere        | 5.91               | 0.506       | 0.578    | 0.532       |\n| MVSFormer          | 7.61               | 0.493       | 0.598    | 0.528       |\n| OMNI-DC            | 8.36               | 0.448       | 0.540    | 0.479       |\n\n\n\n---\n\n>**Q3: The experiments primarily focus on standard benchmarks, but the method’s performance in extreme scenarios (e.g., highly dynamic environments, low-light conditions, or extremely large-scale scenes) remains unaddressed. The paper does not provide evidence of its effectiveness beyond the tested datasets, limiting confidence in its broader applicability.**\n\nWe acknowledge that dynamic environments and extremely large-scale scenes represent important challenges, but these are beyond the scope of this paper. Our focus is on evaluating performance across standard benchmarks that are commonly used to compare state-of-the-art methods (e.g., 3DGS, GOF, PGSR, Neuralangelo, etc), which also do not explicitly address these extreme scenarios. We consider the extension of our framework to dynamic or large-scale settings as an exciting direction for future work. Here we provide a preliminary experiment, based on the VastGaussian implementation for large-scale scenes. We follow VastGaussian to divide the large-scale scene into $2\\times2$ blocks to train our base model and Eve3D. As shown in the table, Eve3D still improves the large-scale surface reconstruction quality.\n\n\n**Table: Eve3D on VastGaussian CUHK UPPER scene of GauU-Scene dataset**\n\n| Method                           | VastGaussian + our base model | VastGaussian + Eve3D | CityGaussianV2 |\n|----------------------------------|-------------------------------|------------------------|-----------------|\n| F1 Score ↑                   | 0.416                         | **0.519**              | 0.492           |\n\n---\n\n\n>**Q4: How would the virtual camera affect the final results? Are the final results sensitive to the different choice of virtual cameras? An ablation study of different virtual cameras should be conducted.**\n\n\n\nIn the submission, we set the baseline length as 7 \\% of the scene radius for all scenes. We assess the impacts of using different baseline lengths in the following table, which illustrates that our method is robust to baseline length selections. Our method optimizes the prior depth under multi-view consistency constraints, thereby increasing the robustness to the initial priors. We will add more details of baseline length in the revised appendix.\n\n\n**Table: Impacts of baseline length**\n\n| Baseline Length         | 3% camera extent | 7% camera extent | 10% camera extent |\n|--------------------------|------------------|------------------|-------------------|\n| F1 Score ↑           | 0.580            | 0.581            | 0.581             |\n\n\n---\n\n\n>**Q5:The generalizability of this method should be well studied, as depth estimation may fail to predict the right depth scale. Are the results sensitive to the initialization of depth estimation?**\n\n\nWe study this in the appendix (Table 9), where we evaluate our method with various sources of depth priors: alternative stereo networks (Stereo Anywhere), multi-view stereo methods (MVSAnywhere and MVSFormer), and a depth completion network (OMNI-DC). \n\nIt is worth noting that all of these methods predict depth maps at the correct scale, although according to different working principles: stereo models estimate disparity maps from stereo images rendered using intrinsics and extrinsics data at the same scale as the pretrained 3DGS, therefore disparity is triangulated into depth using the known focal length from 3DGS and the baseline used during rendering; multi-view stereo methods exploit the same camera poses used to optimize the 3DGS, therefore predicting depth maps at the same scale; finally, depth completion models predict dense depth maps starting from sparse measurements -- i.e., COLMAP points used to initialize 3DGS -- thus maintaining the correct scale.\n\nDespite variations in depth accuracy across these sources, our joint optimization consistently improves reconstruction performance. This demonstrates that our method is robust to different depth initializations and generalizes well beyond any specific depth estimation model. For our main results, we use FoundationStereo, which provides high-quality priors and strong generalization across datasets, as also shown in its original paper.\n\nFinally, our design allows easy integration of future, more robust depth predictors, with the potential to improve reconstruction quality further.\n\n\n\n---\n\n>**Q6: Why did you choose depth priors other than normal or normal \\& depth?**\n\nTo compute the neighbor-view image correspondences for local bundle adjustment, our method requires depths as priors. In the current implementation, since we use depth priors with accurate scales, we use both depth priors and normal vectors computed from depth priors for optimization.\n\n---"}, {"review_id": "yTsKkKC0Zf", "reviewer": "Reviewer_wAXX", "summary": "Method: \n1) run stereo matching on pairs of images with existing network [37]\n2) include depth priors into 3dgs reconstruction and run joint optimization / bundle adjustment to obtain 3dgs scene representation\n\nCore contribution is the use of a specific stereo matching network for better 3dgs reconstructions.", "questions": "- Is the stereo matching network trained on the respective datasets? How do you make sure there is no overlap in the train/test split?\n- How much do the priors help vs how much the joint optimization? I.e., I would like to see another entry in Table 1 and 2.\n- What if the baselines such as 2DGS would have the prior as a helper? I understand that there is some engineering to be done there as we well which this method contributes but at the same time the loss formulation is not that complicated either.\n- How would the surface look like if just the respective stereo depth maps were fused into a joint 3D scene representation (i.e., no 3dgs at all)? E.g., using something like a SDF-based reconstruction framework such as KinectFusion variants or Poisson Surface Reconstruction.\n\nI'm generally on the fence so I would like to see how the authors respond to the questions above.", "limitations": "yes, there is a limitation section.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n- compelling results and comparisons against the baselines in Table 1 and 2.\n- combining existing priors for surface reconstruction and 3DGS is always interesting.\n\nWeakness:\n- in the video, only a single scene is shown. The rest are mostly single objects which is a bit boring; the video rendering quality could be also better.\n- algorithmically, the method is straightforward and it seems the majority from the improvements come from better engineering; e.g., which depth estimation prior to use etc.\n- would be nice to see more ablations there how much that matters (also see below in the question section)", "was_revised": true, "final_justification": "I thank the authors for their rebuttal and additional comments. In combination of reading the other reviews, I would like to keep my original rating and I'm leaning positive towards the paper.", "rebuttal": "### **Responses to Reviewer wAXX** ###\n\nWe thank the reviewers for their insightful feedback. Below we address the questions and concerns raised.\n\n---\n\n>**Q1: In the video, only a single scene is shown. The rest are mostly single objects which is a bit boring; the video rendering quality could be also better.**\n\n\nWe thank the reviewer for the constructive feedback. While the rebuttal roles do not allow us to include additional videos or supplementary materials, we fully agree with this suggestion and will enhance the video with more diverse scenes and improved rendering quality.\n\n\n---\n\n\n\n>**Q2: Algorithmically, the method is straightforward and it seems the majority from the improvements come from better engineering; e.g., which depth estimation prior to use etc.**\n\n\nThanks for the feedback. We'd like to declare that the significant improvement from our method comes from our novel and effective designs of parameterized learnable depth and local bundle adjustment for 3DGS-prior joint optimization, rather than the engineering on depth priors. As shown in Table 4 and Figure 6, our method can consistently improve the performance significantly, demonstrating the general criticalness and effectiveness of our strategies. Furthermore, Figure 8 in the appendix demonstrates that, under the same depth prior, our approach achieves faster convergence and higher reconstruction quality-highlighting the importance of how the priors used, not just which prior is selected.\n\n\n---\n\n>**Would be nice to see more ablations there how much that matters (also see below in the question section)**\n\nThanks for the feedback. Responses to the reviewer’s specific questions regarding these ablations are provided in detail below.\n\n---\n\n>**Q3: Is the stereo matching network trained on the respective datasets? How do you make sure there is no overlap in the train/test split?**\n\nThank you for pointing this out. The selected FoundationStereo network was not trained on our evaluation datasets. In fact, the DTU, Tanks \\& Temples and Mip-NeRF360 datasets do not contain binocular stereo pairs, which makes them unsuitable for training a stereo matching network. FoundationStereo was trained on: Scene Flow, Sintel, CREStereo, FallingThings, InStereo2K and Virtual KITTI 2, none of which overlap with our test datasets. This ensures there is no data contamination and validates the zero-shot generalization capabilities of the stereo foundation model. The same principle applies to the other foundation models used in Table 9 (appendix), ensuring consistency and a fair evaluation protocol.\n\n\n---\n\n\n>**Q4: How much do the priors help vs how much the joint optimization? I.e., I would like to see another entry in Table 1 and 2.**\n\n\nWe agree that disentangling the contribution of the priors from that of the joint optimization is important, and we have already included such an analysis in different parts of the paper. In particular, Table 4 (main paper) compares row 2 (using the learned priors only, without joint optimization) to row 3 (with joint optimization), thereby isolating the contribution of the joint optimization step, showing an F-score improvement from 0.553 to 0.581. And the qualitative comparisons of the reconstructed mesh are shown in Figure 6. Additionally, Table 6 (appendix) quantifies how joint optimization improves the depth priors themselves.\n\n---\n\n\n>**Q5: What if the baselines such as 2DGS would have the prior as a helper? I understand that there is some engineering to be done there as we well which this method contributes but at the same time the loss formulation is not that complicated either.**\n\n\nWe thank the reviewer for raising this point. We run experiments with 2DGS by integrating the same depth priors (FoundationStereo) we use for Eve3D. Furthermore, we also implement our whole Eve3D pipeline assuming 2DGS as the backbone. We present the results achieved by these two frameworks on Tanks and Templetes in the following table, using the same hyperparameter settings as those in the main paper. Using FoundationStereo priors allows for improving mean F-score achieved by 2DGS from 0.30 to 0.45. Furthermore, by applying the whole Eve3D pipeline to 2DGS allows for further pushing the accuracy from 0.45 to 0.55, getting close to the one by our original Eve3D implementation.\nA similar experiment involving PGSR (the most accurate among the competitors) is shown in Table 8 and Figure 8 of the appendix. Even under identical depth supervision, PGSR still performs worse than our approach. This demonstrates that our improvements are not solely due to the use of priors, but also to how they are integrated and jointly optimized within our framework.\n\n**Table: Eve3D with 2DGS on Tanks and Temples datasets**\n\n| Method                  | Barn | Caterpillar | Courthouse | Ignatius | Meetingroom | Truck | **Mean ↑** |\n|-------------------------|------|-------------|-------------|----------|--------------|--------|-------------|\n| 2DGS                    | 0.36 | 0.23        | 0.13        | 0.44     | 0.16         | 0.26   | 0.30        |\n| 2DGS + FoundationStereo | 0.63 | 0.33        | 0.29        | 0.62     | 0.30         | 0.53   | 0.45        |\n| 2DGS + Eve3D            | **0.69** | **0.43**    | **0.31**    | **0.82** | **0.43**     | **0.61** | **0.55**    |\n\n---\n\n>**Q6: How would the surface look like if just the respective stereo depth maps were fused into a joint 3D scene representation (i.e., no 3dgs at all)? E.g., using something like a SDF-based reconstruction framework such as KinectFusion variants or Poisson Surface Reconstruction.**\n\n\nPerforming direct fusion of stereo depth maps into a joint 3D scene representation, as suggested, requires access to stereo pairs. However, the DTU, Tanks \\& Temples, and Mip-NeRF360 datasets do not provide stereo pairs, which makes 3DGS rendering a necessary prerequisite step to generate these views. The GS2Mesh method, which we re-implement using our pipeline (Mini-Splatting + FoundationStereo), follows exactly this paradigm: after generating stereo pairs from a 3DGS model, it fuses the resulting depth maps via standard reconstruction (e.g., KinectFusion). We address this scenario in Table 7 (appendix) with a fair comparison: even when both GS2Mesh and our approach rely on the same high-quality stereo priors, GS2Mesh struggles in complex scenes, whereas our method achieves significantly better reconstructions. We attribute this improvement to our strategy of treating depth maps as learnable priors, jointly optimized with 3DGS, rather than relying on a fixed post-hoc fusion.\n\n---"}]

Author rebuttal:

Meta-review: This paper proposes a dense 3D reconstruction method leveraging 3D Gaussians. The key idea is to jointly optimize imperfect depth and normal maps predicted by existing models together with 3D Gaussians. In addition, the authors introduce a local bundle adjustment strategy designed to avoid local supervision. The method is thoroughly evaluated and compared against strong baselines on standard public datasets.

During the review process, reviewers raised concerns regarding the clarity of the writing, system complexity, generalization ability, and several technical details. Most of these concerns were adequately addressed during the rebuttal, leading all reviewers to recommend acceptance.

After carefully considering the paper, the reviews, and the authors’ clarifications in the rebuttal, the Area Chair concurs that the paper makes a sufficient contribution of interest to the community. Therefore, I recommend acceptance. The authors are strongly encouraged to integrate the clarifications, additional discussions, and further results from the rebuttal into the final version to improve accessibility and impact.
Decision: Accept (poster)