VA-GS: Enhancing the Geometric Representation of Gaussian Splatting via View Alignment
Authors: Qing Li
Huifang Feng
Xun Gong
Yu-Shen Liu
Venue: NeurIPS 2025 poster ()
Keywords: Gaussian Splatting
Neural Rendering
Surface Reconstruction
Novel View Synthesis
Primary area: applications

Abstract: 3D Gaussian Splatting has recently emerged as an efficient solution for high-quality and real-time novel view synthesis. However, its capability for accurate surface reconstruction remains underexplored. Due to the discrete and unstructured nature of Gaussians, supervision based solely on image rendering loss often leads to inaccurate geometry and inconsistent multi-view alignment. In this work, we propose a novel method that enhances the geometric representation of 3D Gaussians through view alignment (VA). Specifically, we incorporate edge-aware image cues into the rendering loss to improve surface boundary delineation. To enforce geometric consistency across views, we introduce a visibility-aware photometric alignment loss that models occlusions and encourages accurate spatial relationships among Gaussians. To further mitigate ambiguities caused by lighting variations, we incorporate normal-based constraints to refine the spatial orientation of Gaussians and improve local surface estimation. Additionally, we leverage deep image feature embeddings to enforce cross-view consistency, enhancing the robustness of the learned geometry under varying viewpoints and illumination. Extensive experiments on standard benchmarks demonstrate that our method achieves state-of-the-art performance in both surface reconstruction and novel view synthesis. The source code is available at https://github.com/LeoQLi/VA-GS.

VA-GS: Enhancing the Geometric Representation of Gaussian Splatting via View Alignment
Qing Li
Huifang Fengå* Xun Gong' Yu-Shen Liu
Southwest Jiaotong University, Chengdu, China
2 Xihua University, Chengdu, China 3 Tsinghua University, Beijing, China
ginglidsujtu. edu. fhf edu.
xgongdaujtu.
Jinyushen@tsinghua.ce
Abstract
3D Gaussian Splatting has recently emerged as an efficient solution for high- quality and real-time novel view synthesis. However, its capability for accurate surface reconstruction remains underexplored. Due to the discrete and unstructured nature Gaussians, supervision based solely on image rendering loss often leads to inaccurate geometry and inconsistent multi-view alignment. In this work, we propose novel method that enhances the geometric representation 3D Gaussians through view alignment (VA). Specifically, we incorporate edge-aware image cues into the rendering loss to improve surface boundary delineation. To enforce geometric consistency across views, introduce visibility -aware photometric alignment loss that models occlusions and encourages accurate spatial relationships among Gaussians. To further mitigate ambiguities caused by lighting variations, we incorporate normal-bare based constraints to refine the spatial orientation of Gaussians and improve local surface estimation. Additionally, feature embeddings to enforce cross-view consistency, enhancing the robustness of the learned geometry under varying viewpoints and illumination. Extensive experiments on standard benchmarks demonstrate that our method achieves state- of-the-art performance in both surface reconstruction and novel view synthesis. The source code is available at https:/stthubbcom/LooQQUVV
leverage deep image
Introduction
Accurate surface reconstruction from multi-view images is a long-standing problem in computer vision, fundamental to applications such as 3D modeling, AR/VR, and robotics. Recently, 3D Gaussian Splatting (3DGS) has emerged as a powerful explicit representation for real-time novel view synthesis, demonstrating impressive rendering quality and speed by modeling scenes collections of semi-transparent 3D Gaussian primitives. However, despite its rendering advantages, 3DGS remains limited in its ability to recover accurate and detailed geometry, especially when supervision is derived solely from RGB images. This limitation stems from the inherent discrete and unstructured nature of Gaussians, which makes it difficult to enforce global surface consistency capture fine geometric details, particularly under complex illumination and along object boundaries.
Existing methods have attempted to enhance the geometric capabilities of Gaussian splatting. For example, SuGaR [14] constructs a density field from Gaussians and extracts meshes via level-set searching, but it struggles with large smooth surfaces and is computationally expensive. 2DGS [16] models scenes using 2D oriented planar Gaussian disks, which inherently represent surfaces and pro- vide view-consistent geometry. However, 2DGS has difficulty reconstructing background geometry and often produces incomplete or distorted surfaces in complex or unbounded scenes. GOF [55]
constructs opacity field from Gaussians and extracts surfaces using Marching Tetrahedra [0
*Conreponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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yielding adaptive mesh resolution without volumetric fusion. Nonetheless, thin structures can be lost and strong lighting contrasts still cause artifacts. GS-Pull [58] integrates neural signed distance field (SDF), dynamically pulling Gaussians toward the zero-level set of the learned SDF. While this improves surface completeness, introduces additional network complexity, produces overly smooth surfaces, and primarily focuses on foreground object reconstruction. PGSR [4] fits Gaussians to local planar hypotheses and uses unbiased depth rendering to improve geometric accuracy. However, does not fully resolve the challenges posed by complex lighting and remains sensitive to boundary ambiguities in non-planar regions. Overall, previous methods have introduced geometric regularizers or hybrid representations and achieved significant progress. However, they still struggle address two persistent challenges: illumination-indee accurate surface boundary delineation, as shown in Fig. Illumination effects distort photometric losses, while ambiguous boundaries often result in geometry drift or holes.
artifacts (e.g., shadows and specular highlights) and
In this work, we propose a novel method for accurate and detailed sur- face reconstruction by enhancing the geometric representation 3D Gaus- sians. We address the limitations of previous methods by incorporating multi-faceter geometric constraints and structural priors. Our approach in- constraints
2DGS
GOF
Ours
troduces geometry- guided by image edges, multi-view alignment that considers visibility and occlusion, and robust priors derived from surface normals and deep image features to mitigate the effects of light- ing variations and boundary ambigui- ties. Specifically, we enhance the standard rendering loss with edge-aware image cues, which sharpen surface boundaries in the 2D projection space of Gaussian splats, resulting in clearer and more precise delincations in rendered images. To enforce geometric consistency across views, we introduce a multi-view photometric alignment loss that explicitly accounts for visibility and occlusions, encour- aging accurate spatial relationships among 3D Gaussians and improving boundary localization. To further reduce ambiguity caused by lighting, we introduce normal-based alignment to constrain the spatial orientation of Gaussians, ensuring reliable surface estimation. Additionally, we leverage high- dimensional image features to enforce cross-view consistency, improving robustness to viewpoint and lighting variations. These innovations significantly reduce the impact of complex illumination and boundary ambiguity, enabling accurate surface reconstruction in challenging scenes. Experiments on standard benchmarks demonstrate that our method achieves state-of- the-art performance in both surface reconstruction and novel view synthesis. Our contributions are summarized as follows.
Figure 1:
Our method addresses illumination and boundary artifacts that previous methods fail to resolve.
Incorporating edge information and visibility-aware multi-view alignment to enhance surface boundary delineation and improve geometric consistency.
Aligning the robust priors based on normals and deep image features to mitigate illumination- induced artifacts and increase reconstruction accuracy.
State-of-the-ard results on standard benchmarks, demonstrating the effectiveness of our method in both surface reconstruction and novel view synthesis.
2 Related Work
View Synthesis and Gaussian Splatting, Neural Radiance Fields (NcRF) 31 pioncered high-fidelity novel view synthesis by representing a scene as a continuous volumetric density and view-dependent color field, optimized via differentiablee volume rendering. Subsequent works accelerated training and rendering through hybrid representations such multi- hash grids (32), explicit voxel or sparse tensor grids [40 24 and learned feature planes [52]. However, these volumetric methods still
entail high memory and computational costs. 3DGS 21 departs from dense volumes by modeling a
scene as a sparse cloud of anisotropic 3D Gaussians, Follow-up work has enhanced visual fidelity
through anti-aliasing and level-of-detail control 54
38 improved training speed and robustness
under sparse views using density regularization and learned radiance priors [46] [34], and extended
3DGS to dynamic scenes 229 47 relighting [1]] and animation [50]. Geometry- aware variants such

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as FatesGS [77 DNGaussian 23 and GeoGaussian 126 address sparse-view and textureless regions, while methods like Instantsplat [T] and Scaffold-GS 28 accelerate convergence by leveraging pretraining hybrid implicit-epllictt designs. Despite these advances, most 3DGS variants primarily emphasize appearance quality and lack mechanisms to enforce explicit surface geometry, motivating dedicated reconstruction techniques.
Surface Reconstruction with Gaussians. Extracting accurate surfaces from a 3DGS representation is challenging due to its unstructured nature and supervision based solely on RGB signals. Early approaches convert Gaussians into volumetric density or opacity fields: SuGaR [4] builds a density field and applies level-set search with Poisson reconstruction [20]. but it struggles to recover large, smooth surfaces; GOF [55] accumulates per-view alpha values into an opacity volume and extracts iso-surfaces with Marching Tetrahedra [T0], achieving adaptive resolution but often missing fine, thin structures under high lighting contrast. Other methods project Gaussians into oriented 21) disks (surfels) and fuse via Truncated Signed Distance Function (TSDF) fusion
3 or Poisson reconstruction. 2DGS [T6] and GSurfels [8] improve local alignment but tend to introduce distortions in unbounded scenes and result in incomplete background geometry. PGSR [4) fits Gaussians to planar patches and adds multi-view photometric and geometric regularization, excelling on planar man-made scenes but remaining sensitive to non-planar boundaries. More recent works [53
[58
30
1
24 25
integrate Signed Distance Fields (SDF) to guide Gaussian placement. GSDF
[53] and 3DGSR 300
jointly optimize neural SDF branch alongside Gaussian parameters using allume-rendered depth and normal supervision, which improves surface smoothness but requires additional network branches.
GS-Pull [58] leverages SDF gradients pull Gaussians toward the zero-level set, enhancing alignment at the cost of limiting object-level reconstruction and producing overly smooth results. Methods impose priors on Gaussians but rely on TSDF fusion's 's fixed resolution or Poisson reconstruction' 's sensitivity to noisy inputs, and
that incorporate depth normal estimators 5 56
45
4
often struggle under varying illumination around complex geometric boundaries. Our approach enforces view consistency through multi-faceted constraints during Gaussian optimization, enabling high-fidelity mesh extraction even under challenging lighting and boundary conditions.
3 Preliminaries
explicitly represents scene collection of anisotropic 3D Gaussians, which can be rendered to images from arbitrary viewpoints using a splatting- based rasterization technique [59]. Specifically, each 3D Gaussian G is defined as:
3DGS (2
G(z) = exp (.5(==)))) 1-
(1)
where ja is the Gaussian center and Σ is its covariance matrix. For novel-view rendering, the color pixel p is obtained by compositing K ordered Gaussian splats using point-based a-blending, iee.,
K
C(p)
Σ
C:o0 II j=1
a1-a),
where a; denotes the pixel translucency determined by the learned opacity of the i-th Gaussian kernel and its projected footprint at pixel The view-dependent color is encoded using spherical harmonics associated with each Gaussian. In addition color, Eq. ( is similarly used to render per-pixel normals and depths by replacing C; with the corresponding normal or depth value.
Normal and Depth Estimation from Gaussians. The covariance matrix Σ E R3x3 of 3D Gaussian can be decomposed into a rotation matrix R and a scaling matrix S, i.e., Σ RSS RT. where R contains the three orthogonal eigenvectors, and S encodes the scale along these directions. This decomposition resembles an ellipsoid representation: the eigenvectors define the axes of the ellipsoid, while the scale values correspond to the axis lengths. As optimization progresses, the initially spherical Gaussian flattens and approaches plane [19]. We take the direction corresponding to the smallest scale factor as the normal n of the Gaussian. The distance from the local plane to the camera center is then computed as d = (R, (μ T.)) T(R, n.). where R. is the rotation from the camera to the world frame, and T, is the camera center in world coordinates. Given the normal and distance, the depth is obtained by intersecting the viewing ray with the local plane: = d/(R.n n K p)), where p is the homogeneous coordinate of the pixel (we use p to denote both the homogencous and 2D pixel coordinates for simplicity), and K is the intrinsic matrix of
3

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3D Gaussian
Souree
Depth
Occlusion w
Rasterizer
(K, M)
Visibility
Reference
Rasterizer
GT Reference I,
Color
Normal
Distance
Depih
H
Liss
Lus
Normal
GT Source I,
Lj
Pp
Figure 2: Overview of our method. The training includes five loss functions: Lr, Licr Lus, Lp and Cy. The occlusion weight w, visibility item and homography matrix H are involved in Lp and Cy. The image features F, and F, are extracted using a pretrained network f. {K, M) the intrinsic/extrinsic parameter matrix of the camera view.
the camera. Finally, the per-pixel distance, depth, and normal maps under the current viewpoint are rendered using a-blending as defined in Eq. where the attribute color c, is replaced with the corresponding Gaussian attributes.
4
Method
Fig. 2 iilustratts the overall framework of our approach. Given set of posed RGB images, our goal to learn bunch of 3D Gaussian functions with associated attributes, such color, opacity, position and shape, to represent the geometry of 3D scene. We introduce novel constraints to enable accurate surface reconstruction while preserving high-quality novel view synthesis.
4.1 Single-Vies View Alignment
Edge-aware Image Reconstruction. The original 3DGS [1 and its variants typically employ a color rendering loss, which combines the reconstruction error with a D-SSIM term. While effective for overall image quality, this loss alone is insufficient for accurately capturing object boundaries during surface reconstruction, and it tends to overly smooth high-frequency regions and complex structures. To address this limitation, propose edge-aware image reconstruction loss that encourages the model to better preserve sharp structures and boundary details:
= 31 L,
31 LSSIM - I) + 32 (Vi - VT),
where the rendered image. is the ground-truth image, and VI denotes the image gradient normalized the range [0, 1]. 31 and B3 are weight factors. The incorporation of muddentt-bseed supervision leads to better preservation of object contours and improves reconstruction quality in boundary and texture-rich regions.
Normal-based Geometry Alignment. 2DGS [16] introduces a normal consistency loss that aligns the normals of Gaussian primitives with those derived from the rendered depth map, ensuring that each 2D splat locally approximates the underlying object surface. However, in boundary regions, the Gaussian primitives often exhibit ambiguous normal directions due to insufficient local support, which can lead to inaceurate geometry reconstruction across different surfaces. To address this issue, utilize image edges as proxies for geometric edges, assuming that areas with strong image

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gradients are likely to correspond to surface discontinuities. Thus, we adopt an edge-aware normal consistency loss defined as:
Luc = - Σ N N 11 PEZ
where 6 = (1 VI)² serves as per-pixel weight that downweights loss contributions from edge regions, and I denotes the set of image pixels. N is the rendered normal, and N is the normal estimated from the depth map gradient [T6]. To compute normal N, we first project four neighboring depth samples into 3D points in the camera coordinate system. We then estimate the surface normal at pixel p by computing the cross product of vectors formed from these projected points, effectively fitting a local plane.
While the above loss enforces the global alignment of Gaussian primitives with the actual surface, noisy primitives still appear in flat texture-less regions, leading abrupt and unnatural changes in surface normals. Moreover, illumination changes, such as shadows shown in Fig. D may introduce false edges during reconstruction. To address these, we use normal smoothing loss that encourages local continuity of surface normals by penalizing large discrepancies between adjacent pixels:
Lus
Σ 8% L..k
R R
(IN Nasi -x)) [|N NGnnl- -
(5)
where ((.)) and ((.)) denote the normals at pixel location (i.j), and & E {(t 1,J), (t,j + 1)} refers to its neighboring pixels in the horizontal and vertical directions. R(-) is the ReLU function, and [-] denotes the Iverson bracket, which evaluates to if the condition inside is true and 0 otherwise. The threshold T and weight å help distinguish surface edges and prevents over-smoothing in high- freeuerryyency regions. This loss promotes smoother local geometry while preserving meaningful structural edges, thereby improving the overall surface fidelity.
4.2 Multi-View Alignment
Multi View Photometric Alignment. While image reconstruction and geometry alignment losses help reduce artifacts and preserve coarse geometry, they often fail to capture fine details. To address this, we draw inspiration from traditional multi-view stereo (MVS) methods [37 3 [2]. which refine surfaces by enforcing photometric consistency across views. Specifically, they project 3D points derived from depth maps onto multiple views and compare their colors to evaluate consistency. By introducing photometric consistency loss based on plane patches, we leverage multi-view observations resolve geometric ambiguities, particularly at object boundaries, and enhance reconstruction accuracy.
As shown in Fig. 2 let I, be the reference view image, and I, E [Ts.i | = = 1.2.... N) denote its neighboring source views. For a pixel p, in the reference view, we define its corresponding plane by normal n, and distance d,.. Using homography matrix HYS, pr is projected to p in the source view as follows:
p-. = H,s Pr. H,, = K2 R.. Tr,n d,
K
where R2 and T,. are the relative rotation and translation from the reference to the source view. Assuming local planarity, warp reference patch P, centered at p. its corresponding source patch P. using H,s. We enforce multi-view photometric alignment by encouraging consistency between P, and P.:
c- Σ
Σ
(rr (w()))-(p))))))) -1 (1-C C(PCC(P())),,, P.(p))), i= 1,2, N, (7)
LE D.EI,
where C(-) is the normalized cross-correlation 51 and V is the number of visible pixels. The visibility term (p+(pr) indicates whether p, is visible in the source view, and w(p,) is weight accounting for geometric occlusion. Note that we aggregate the losses from all source views by summation, averaging. The definitions of (p.) and (p.) detailed in the following.
Due viewpoint changes, 2D pixel p, in the reference view may fall outside the field of view when projected into source view. We define a visibility term Ura (pr) to indicate whether p, is
5

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visible from the source viewpoint. Given pixel p, with rendered depth 2,, its corresponding 3D point x, and projected pixel coordinate p', the source view are computed as:
p'_ 5 (KM, M,
x= = z, Pr
(8)
where M is the extrinsic matrix of the camera, T(-) converts 3D coordinates to 2D pixels. The pixel p, is considered visible in the source view if its projection p'g lies within the image bounds. Thus the visibility term is defined as:
(p.) [(0,0) p's (W, H)],
where H) is the image resolution, and [-] denotes the Iverson bracket.
H)
During projection via the homography matrix, some pixels may be occluded or exhibit significant geometric error [4] To avoid the influence of such outliers, we exclude them from the multi-view alignment loss using an occlusion- -aware weight. Given reference 3D point æ, and its correspondingg
rendered (or interpolated) depth 2, in the source view, we first compute the projection error at p, as:
p(p:) -1 1r D 1l2. H, H. Zs: x²_ M,M, x.
(10)
(11)
where p/ is the reprojected pixel in the reference view, H. denotes the depth normalized version of z/.. We then define the occlusion weight as w(p,) 1/exp(((pp)) if p(pr) < 1, and otherwise
A small projection error indicates reliable geometry, resulting in a higher weight, while large error implies occlusion or misalignment, thus being downweighted or discarded.
Multi- View Feature Alignment. The previously introduced image reconstruction and photometric alignment losses help preserve the shape and structure of the objects. However, image-based losses are susceptible to noise, blur, and low-texture regions. Additionally, due to lighting variations, the color of the same surface point may differ across views, making photometric consistency unreliable. To address these limitations, we introduce multi-view feature alignment loss. We extract image features using a pretrained network f [57), i.e., F f().. Let F, denote the reference view's feature map, and F, be one of the source view features, with F, E (F., the pixel-wise feature alignment loss defined as:
.N.. Then
1 E
Σ - FEFF. P.EI,
Ura (pr -((p) |1 cos (F.(Pr), Fx()))), i= 1, I,,,..., ,N, (12)
where cos(-) denotes the cosine similarity between feature vectors. This feature-level loss improves robustness under challenging conditions such as appearance variation and poor lighting consistency.
Final loss. To summarize, the final training objective integrates five components:
L L, + An Apfns + >gh, + X46
(13)
where d1 X2, As and a are weighting factors determined based on validation performance.
5 Experiments
Evaluation Protocols. We evaluate our surface reconstruction performance on the DTU 18 and Tanks and Temples (TNT) [22] datasets. Following prior works [16 55 4 56], we use 15 scenes from the DTU dataset and scenes from the TNT dataset for evaluation. Depth maps are rendered for all training views, and TSDF 7 is constructed for mesh extraction. For novel view synthesis, we use the Mip-NeRF 360 dataset 21, which contains large-scale indoor and outdoor scenes with complex lighting and -ineinraieed geometric details. Following 3DGS [21). one out of every eight images is used for evaluation, while the remaining seven are used for training. We employ COLMAP (36] to generate sparse point cloud from the original dataset images for initializing the 3D Gaussians. All images are downsampled to a lower resolution to facilitate training. Following established protocols [16 [55 4 56), we report Chamfer distance for surface reconstruction on the DTU dataset and Fi-score for the TNT dataset. For novel view synthesis, we evaluate using three widely adopted image quality metrics: PSNR, SSIM, and LPIPS.
Implementation Details. Our overall pipeline, training strategy, and hyperparameter settings generally follow 3DGS (21). We set the number of source views to N 3,, the threshold in C.s

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GS-Pull
2DGS
GOF
POSR
RaDe-GS
Ours
Figure 3: Visual comparison of surface reconstruction results on the TNT dataset. Our method can handle shadows and large indoor flat regions. GS-Pull reconstructs only the foreground objects.
GS-Pull
2DGS
GOF
PGSR
RaDe-GS
Ours
Figure 4: Visual comparison of surface reconstruction results the Mip-NeRF 360 dataset. Our approach effectively handles the challenges posed by cluttered lighting and boundaries.
to -= 0.01, and the patch size in Lp to 7x 7. The loss weight factors are set as follows: 31 1 0.2, 82 0..0, À 00015, A2 0.3, Aa 0.155, and A 1.0. The model is trained for 20,000 iterations for surface reconstruction and 30,000 iterations for novel view synthesis. We first pretrain the model using only the color loss for 7,000 steps obtain a coarse geometric initialization, which provides stable foundation for subsequent geometry refinement. Then, we incorporate our image edge item and normal-based geometry alignment into the training. To further refine geometry, we sequentially apply our multi-view photometric alignment for 8,000 iterations, followed by 5,000 iterations of multi-view feature alignment. For novel view synthesis, we continue training for additional 10,000 steps to optimize rendering quality. All experiments are conducted on a single NVIDIA RTX 4090 GPU.
5.1 Performance Evaluation
Comparisons on DTU. We first compare our method with state-of-the implicit and explicit surface reconstruction approaches on the DTU dataset [18]. Following standard protocol, reconstructions are clipped using the provided mask, and evaluations are performed only foreground objects, as the ground truth point clouds exclude background regions. As shown in Table our method achieves the lowest average Chamfer distance and ranks best across most scenes. Compared to implicit approaches
7

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Table 1: Quantitative comparison of Chamfer distances the DTU dataset. The best results
highlighted
1st
2nd
and 3rd
means that the source code is not available.
24
37
40
55
63
65
69
83
97
105
106
110
114
118
122
Mean
Time
NeRF 31
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
> >12h
VolSDF
48
1.14
1.26
0.81
0.49
1,25
0.70
0.72
1.29
1.18
0.70
0.66
1.08
0.42
0.61
0.55
0.86
>12h
Implicit
NeuS 44
1,00
1.37 0.93
0.43
1.10
0.65
0.57
1.48
1.09
0.83
0.52
1.20
0.35
0.49
0.54
0.84
> 12h
WeuralWarp
9
0.49 0.71 0.38
0.38
0.79
0.81
0.82
1.20
1.06
0.68
0.66
0.74
0.41
0.63
0.51
0.68
> 10h
Neuralangela
0.37 0.72 0.35
0.35
0.53
1.29
0.97
0.47
0.74
0.32
0.43
0.61
>12h
PSDF 39
0.36 0.60 0.35
0.36
00770
0.61
0.49
1.11
0.89
0.60
0.47
0.57
0.30
0.40
0.37
0.55
3DGS 21
2.14 1.53 2.08
1.68
3.49
2.21
1.43
2.07
2.22
1.75
1.79
2.55
1.53
1.52
1.50
1.96
3.4m
SuGaR [4]
1.47 1.33 1.13 0.61
2.25
1.71
1.15
1.63
1.62
1.07
0.79
2.45
0.98
0.88
0.79
1.33
lh
GaussianSuriels
8
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
0.53
0.88
4.5m
2DGS [66
0.48 0.91 0.39 0.39
1,01
0.83
0.81
1,36
1.27
0.76
0.70
1.40
0.40
0.76
0.52
0.80
5.8m
Explicit
GS-Pull [58
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
0.88
0.42
0.75
5.6m
GOF [55]
0.50 0.82 0.37 0.37 1,12
0.74
0.73
1.18
1.29
0.68
0.77
0.90
0.42
0.66
0.49
0.74
32m
RaDe-GS
0.46 0.73 0.33 0.38 0.79 0.75
0.76
1.19
1.22
0.62
0.70
0.78
0.36
0.68
0.47
0.68
6.5m
POSR 4
0.34 0.58 0.29 0.29
0.54
1:01
0.73
0.49
0.69
0.37
0.53
15m
GausSurl
43
0.35 0.55 0.34
0.34
0.77
0.58
0.51
1,10
0.69
0.60
0.43
0.49
0.32
0.40
0.37
0.52
Ours
0.32 0.49 0.32 0.30 0.77 0.68
0.43
1.05
0.61
0.57
0.36
0.52
0.28
0.33
0.30
0.49
15.5m
Table 2: Quantitative comparison of Fl-scores on the TNT dataset. The best results are highlighted
as
1st
2nd and 3rd means that the source code is not available.
Barn
Caterpillar
Courthouse
Ignatius
Mectingroom
Truck
Mean
Time
NeuS 47
0.29
0.29
0.17
0.83
0.24
0.45
0.38
> >12h
Implicit
Geo-Neus
12
0.33
0.26
0.12
0.72
0.20
0.45
0.35
> 12h
Neuralangelo
27
0.70
0,36
0.28
0.89
0.32
0.48
0.50
12hh
PSDF 39
0.62
0.39
0.42
0.79
0.47
0.53
0.53
3DGS 21
0.13
0.08
0.09
0.04
0.01
0.19
0.09
7.5m
DN-Splatter
42
0.15
0.11
0.07
0.18
0.01
0.20
0.12
20m
SuGaR 44
0.14
0.16
0.08
0.33
0.15
0.26
0.19
2h
GaussianSurfels
3
0.24
0.22
0.07
0.39
0.12
0.24
0.21
5m
Explicit
2DGS [6
0.41
0.23
0.16
0.51
0.17
0.45
0.32
7.5m
GS-Pull [58)
0.60
0.37
0.16
0.71
0,22
0.52
0.43
18m
GOF [55]
0.51
0.41
0.28
0.68
0.28
0.59
0.46
40m
RaDe-GS 56
0.43
0.32
0.21
0.69
0.25
0.51
0.40
9m
PGSR 4
0.66
0.44
0.20
0.81
0.33
0.66
0.52
25.5m
GausSurf"
43
0.50
0.42
0.30
0.73
0.39
0.65
0.50
Ours
0.71
0.45
0.21
0.82
0.40
0.64
0.54
20.6m
such as NeuS 44 and Neuralangelo [27]. our method delivers significantly better reconstruction accuracy while being much more efficient in terms of runtime. It is worth noting that most implicit methods 44 27 only reconstruct foreground geometry, whereas our approach can produce detailed and complete meshes, including background regions, which is an essential feature for mesh based rendering. Although our method is slightly slower than 3DGS 21 and 2DGS [66] due to the use of multi-vied view alignment, it achieves significant improvements in reconstruction quality over these earlier Gaussian- based methods.
Comparisons on TNT. We further evaluate our method on the TNT dataset [22] comparing it against both implicit and explicit surface reconstruction baselines. Since the ground-truth point clouds do not include background regions, the evaluation is restricted to foreground objects. As shown in Table 2 our method achieves the best reconstruction performance among all competing approaches, including both implicit and explicit methods. Notably, while several catussian-basee methods require less optimization time, they tend produce results with much lower accuracy. In contrast, our method reaches a better balance between efficiency and reconstruction quality. For example, Gull [58] only reconstructs foreground objects and often generates overly smooth surfaces. Fig. B provides a qualitative comparison. Our method produces more accurate and detailed reconstructions for both foreground and background regions. also effectively mitigates the impact of shadows, whereas baseline methods often yield noisy meshes or fail to capture geometric details. The use of geometry, photometric, and feature-based based alignment from multiple views provides strong guidance, enabling the Gaussian primitives to converge more accurately to the true surface geometry.

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Table 3: Quantitative comparison on the Mip-NeRF 360 dataset. The best results are highlighted as
1st
2nd and 3rd
Outdoor scenes
Indoor scenes
Average all scenes
PSNR
SSIM
LPIPS 4
PSNR
SSIM
LPIPS
PSNR t SSIM f LPIPS +
NeRF 31
21.46
0.458
0.515
26.84
0.790
0.370
23.85
0.606
0,451
Deep Blending
15
21.54
0.524
0.364
26.40
0.844
0.261
23.70
0.666
0,318
Instant NGP 32
22.90
0.566
0.371
29.15
0.880
0.216
25.68
0.706
0.302
MERF 35
23.19
0.616
0.343
27.80
0.855
0.271
25.24
0.722
0.311
BakedSDF
40
22.47
0.585
0.349
27.06
0.836
0.2.8
24.51
0.697
0.309
Mip-NeRF
2
24.47
0.691
0.283
31.72
0.917
0.180
27.69
0.791
0.237
3DGS 21
24.64
0.731
0.234
30.41
0.920
0.189
27.20
0.815
0.214
SuGaR 14
22.93
0.629
0.356
29.43
0.906
0.225
25.82
0.752
0.298
2DGS [6]
24.34
0.717
0.246
30.40
0.916
0.195
27.03
0.805
0.223
GS-Pull 58
23.76
0.703
0.278
30.78
0.925
0.182
26.88
0,802
0,235
GOF 55
24.82
0.750
0.202
30.79
0.924
0.184
27.47
0.827
0.194
RaDe-GS
56
25.17
0.764
0.199
30.74
0.928
0.165
27.65
0.837
0.184
PGSR
24.45
0.730
0.224
30.41
0.930
0.161
27.10
0.819
0.196
GausSurf
45
25.09
0.753
0.212
30.05
0.920
0.183
27.29
0.827
0.199
Ours
25.00
0.760
0.191
30.63
0.933
0.153
27.50
0.837
0.174
Comparisons on Mip-NeRF 360. We also evaluate our approach on the Mip-NeRF 360 dataset for novel view synthesis. Table reports quantitative comparisons against state-of-the-arr Gaussian- based and other neural rendering baselines. Our method outperforms competitors most metrics, demonstrating superior image fitting and generalization to unseen viewpoints. This evidences that enhanced geometry representation yields higher visual fidelity. Notably, the Mip-NeRF 360 itself achieves the highest average PSNR on indoor scenes but lags on SSIM and LPIPS. Among Gaussian- based methods, 2DGS [16]. SuGaR [14), and GS-Pull [58] perform worse than vanilla 3DGS (I], suggesting that their planar Gaussian constraints degrade performance in complex environments. Our ablation results in Table 4 further confirm that flattening 3D Gaussians into planar Gaussian disks is ineffective for our framework. Our method preserves the full 3D Gaussian representation and delivers high-quality surfaces without sacrificing novel-view rendering quality. Fig. provides qualitative comparison of reconstructed meshes. Consistent with our observations on the TNT dataset, our method recovers more accurate and complete surfaces in both foreground and background regions, whereas other methods suffer from noise, oversmoothing, or missing details, especially in challenging indoor scenes.
5.2 Ablation Studies
Table 4:
Ablations on the TNT dataset.
To quantify the contributions of our alignment constraints, we perform ablations by selectively removing loss terms and report reconstruction quality on the TNT dataset. In addition to the Fl-score, we also report Precision and Recall to provide more comprehensive evaluation. The base color rendering loss from 3DGS always retained in the following experiments. We pro- vide quantitative results in T
Precision
Recall
Fl-score
Only Cr w/o edge item
0.09
0.23
0.13
0.49
0.59
0.53
w/o weight ō
0.50
0.59
0.53
w/o Lne
0.48
0.60
0.52
w/o Lns
0.47
0.58
0.51
w/o Lne + Lus
0.40
0.57
0.46
w/o Lp
0.46
0.56
0.50
w/o L,
0.49
0.60
0.53
(1) Only image reconstruction loss (C,): Re- moving all alignment losses yields the worst results, with average F1-score of 0.13, but still better than the vanilla 3DGS's score of 0.09. (2) Edge-aware term in L Omitting the image edge-based component slightly degrades perfor- mance, confirming its role in preserving bound- ary detail.
w/o C. + L.g
0.33
0.40
0.36
w/ scale loss
0.51
0.60
0.54
N
0.49
0.58
0.52
N 2
0.49
0.59
0.53
N
0.51
0.60
0.54
Ours
0.51
0.60
0.54
(3) Edge-aware weight 5: In boundary regions, Gaussian primitives often exhibit ambiguous noisy normal directions, which can lead incorrect supervision signals. The weight 8 in loss L.e reduces the loss contribution from these areas, allowing the network to focus learning more reliable surface

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regions. While the improvement is modest, it reflects the fact that shape boundaries constitute relatively small proportion of the scene, and thus affect only small number of sampled points during evaluation.
(4) Normal-based alignment (Ene> Lns): The normal consistency (Cne) and smoothing (Lns) losses are critical. Excluding either term causes noticeable drop in Precision and Fi-score, and removing both leads dramatic performance collapse.
(5) Milw alignment (Cp, f Enforcing photometric and feature consistency across views consistently improves reconstruction accuracy. Each multi-view alignment term contributes positively, validating the benefit of cross-view geometric constraints.
(6) Scale regularization: The scaling matrix S represents the stretching of spherical Gaussian along the three axes. Different from previous works [4] 6 [58, incorporating the widely used scale penalty into our method to flatten the 3D Gaussian disks provides no performance gains, and even degrades novel-view rendering quality on the Mip-NeRF 360 dataset.
(7) Number of source views (N): Our method takes both reference view and NN source views. Increasing the number of source views used in the alignment losses improves reconstruction quality. However, setting N 4 yields additional performance gains but increases the computational cost. We therefore choose N = 3 to balance accuracy and efficiency.
Overall, these ablations demonstrate that each of our proposed alignment constraints plays distinct and essential role in achieving high-fidelity surface reconstruction.
Conclusion
In this paper, we address the limitations of existing 3D Gaussian Splatting approaches in recovering accurate and detailed surface geometry, especially under challenging conditions such as complex lighting and ambiguous object boundaries. We propose a novel method that improves geometric fidelity by integrating edge-aware supervision, visibility-aware multi-view alignment, and robust geometric constraints based on surface normals and deep visual features. These components jointly enforce cross-view consistency, enhance boundary sharpness, and mitigate the impact of illumination- induced artifacts. Extensive experiments demonstrate that our method achieves state-of-the-art performance in both surface reconstruction and novel view synthesis, underscoring its effectiveness and robustness in complex real-world scenarios. The main limitation of our approach is its relatively slower training speed compared to earlier 3DGS variants. In future work, we aim explore adaptive Gaussian pruning and learned covariance regularization to accelerate training and further improve robustness in large-scale and dynamic scenes.
Acknowledgements
This work was supported by the National Natural Science Foundation of China (62402401), the Sichuan Provincial Natural Science Foundation of China (20252NFFSC1462) and the Fundamental Research Funds for the Central Universities (2682025CX109).
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Reviews:
[{"review_id": "8aEiifzX3d", "reviewer": "Reviewer_KaXG", "summary": "This paper proposes VA-GS, a method for enhancing the geometric representation of 3D Gaussian Splatting via view alignment. The approach incorporates five key loss functions to improve surface reconstruction: edge-aware image reconstruction, normal-based geometry alignment, visibility-aware multi-view photometric alignment, and multi-view feature alignment. The method addresses limitations in boundary delineation and illumination-induced artifacts that affect surface quality in standard 3DGS. Experiments on DTU and TNT datasets demonstrate improved surface reconstruction metrics while maintaining novel view synthesis quality.", "questions": "I suggest the author to include comparisons with PSDF to better position the contribution and justify the novelty concern.", "limitations": "Please refer to Questions and Weakness.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n\n1. The paper addresses important limitations of 3DGS by incorporating multiple complementary constraints - edge awareness, normal consistency, photometric alignment, and feature-level consistency - creating a well-rounded solution for geometric enhancement.\n\n2. The method integrates cleanly with existing 3DGS frameworks without requiring architectural changes, making it easily adoptable.  For instance, the visibility-aware photometric alignment is particularly well-designed for handling occlusions.\n\n3. Comprehensive evaluation on standard benchmarks (DTU, TNT, Mip-NeRF 360) shows consistent improvements in surface reconstruction metrics while maintaining competitive novel view synthesis performance.\n\nWeakness\n\n1. Novelty concern.  The proposed technique resembles prior methods such as PGSR and PSDF. While the combination is effective, individual components (edge-aware losses, normal consistency, photometric alignment) are well-established techniques. \n\n2. Missing comparison with PSDF.  Despite being directly related in spirit, the paper does not compare VA-GS with more advanced methods such as PSDF.", "was_revised": true, "final_justification": "As the rebutal has addressed most of my concern, I would like to keep my positive recommendation.", "rebuttal": "### 1. Novelty concern.\n\nUnlike PGSR’s planar‑based splatting approach and PSDF’s neural implicit surface framework, our main innovation lies in the Gaussian-based view alignment strategy, which explicitly aligns Gaussian primitive orientations and projections across multiple views and applies feature-level constraints.\nThis design enables consistent geometry reconstruction even in high-curvature or complex lighting scenarios, which is verified by qualitative comparison results.\n\nIn contrast, PGSR introduces planar splatting of Gaussians and couples photometric consistency with geometric regularization via plane-based homography.\nIt relies on scale regularization for flattening Gaussian ellipsoids and employs supervision with more complex gradient backpropagation paths (embedding geometry into homography).\nPGSR uses a standard combination of L1 and SSIM losses to compute the difference between rendered and reference images, without incorporating our newly proposed edge-aware term.\nIn addition, it does not include our newly introduced normal smoothing loss in its normal-related constraints.\nOn DTU, we reduce Chamfer distance from 0.53 to 0.49; on TNT, we raise F1-score from 0.52 to 0.54; on Mip-NeRF 360, we observe consistent substantial improvements across all metrics (See Tables 1, 2 & 3).\n\nOn the other hand, PSDF is built on a neural implicit SDF representation, leveraging external MVS priors and importance sampling for multi-view consistency.\nIt does not operate on explicit Gaussian primitives, nor does it align oriented Gaussians across views as we do.\nOn DTU, we reduce Chamfer distance from 0.55 to 0.49; on TNT, we raise F1-score from 0.53 to 0.54 (See *Question 2* for details).\n\nIn summary, although our work shares some conceptual goals with prior methods, our implementation and integration of these ideas are fundamentally different in both design and effect.\nOur contributions are novel and distinct: we operate at the level of Gaussian primitive orientation alignment, consolidate single-view and multi-view alignment mechanisms in a unified framework, and achieve performance that exceeds both PGSR and PSDF in quality and efficiency across multiple datasets.\n\n\n### 2. Comparison with PSDF.\n\nWe appreciate the reviewer’s suggestion.\nWe have now included a quantitative comparison with PSDF on both the DTU and TNT datasets.\nAs shown in the following tables, our method outperforms PSDF on most scenes and categories, achieving a lower average Chamfer distance on DTU (0.49 vs. 0.55) and a higher mean F1-score on TNT (0.54 vs. 0.53).\nThis confirms the effectiveness of our method for high-fidelity surface reconstruction.\nWhile PSDF slightly outperforms our method on a few specific categories, we see this as valuable feedback and plan to improve feature extraction and robustness to large view variations in future work.\nNote that the results of PSDF are taken from the original paper, as its source code is not publicly available.\nWe will cite this work and include these comparisons in the revised version.\n\nAdditionally, our method is significantly more efficient.\nAs reported by the PSDF authors, training on TNT takes 17.05 hours on an RTX 3090, whereas our approach completes training in 21 minutes on an RTX 4090.\nThis highlights the practical advantage of our explicit Gaussian-based design over neural implicit SDF frameworks, which typically require long training times (more than ten hours, as shown in Tables 1 & 2 of our paper).\n\n*Quantitative comparison of Chamfer distances on the DTU dataset (lower is better).*\n\n|        | 24 | 37 | 40 | 55 | 63 | 65 | 69 | 83 | 97 | 105 | 106 | 110 | 114 | 118 | 122 | Mean |\n|:-:     |:-: |:-: |:-: |:-: |:-: |:-: |:-: |:-: |:-: |:-:  |:-:  |:-:  |:-:  |:-:  |:-:  |:-:   |\n|PSDF    | 0.36     | 0.60     | 0.35     | 0.36     | **0.70** | **0.61** | 0.49     | 1.11     | 0.89     | 0.60     | 0.47     | 0.57     | 0.30     | 0.40     | 0.37     | 0.55     |\n|Ours    | **0.32** | **0.49** | **0.32** | **0.30** | 0.77     | 0.68     | **0.43** | **1.05** | **0.61** | **0.57** | **0.36** | **0.52** | **0.28** | **0.33** | **0.30** | **0.49** |\n\n*Quantitative comparison of F1-scores on the TNT dataset (higher is better).*\n\n|        | Barn    | Caterpillar | Courthouse | Ignatius | Meetingroom | Truck    | Mean     |\n|:-:     |:-:      |:-:          |:-:         |:-:       |:-:          |:-:       |:-:       |\n|PSDF    |0.62     | 0.39        | **0.42**   | 0.79     | **0.47**    | 0.53     | 0.53     |\n|Ours    |**0.71** | **0.45**    | 0.21       | **0.82** | 0.40        | **0.64** | **0.54** |"}, {"review_id": "FZX6SI9L9I", "reviewer": "Reviewer_3Mxm", "summary": "The paper proposes a method to enhance surface reconstruction in 3D Gaussian Splatting (3DGS) by introducing geometry-aware constraints into the rendering process. The authors incorporate four novel constraints: (1) an edge-aware image reconstruction loss that sharpens object boundaries, (2) a normal-based geometry alignment loss that refines surface orientation while suppressing noise and over-smoothing, (3) a multi-view photometric alignment loss that enforces visibility-aware color consistency across views using local homographies, and (4) a feature alignment loss based on deep features to handle variations in appearance (e.g., due to illumination change). The final objective combines all these losses to jointly optimize geometry and appearance. The method achieves state-of-the-art results on standard benchmarks in both surface reconstruction and novel view synthesis.", "questions": "I am mostly interested in the run-time cost of each introduced loss.", "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\n- The paper is well-executed, with clear ideas and thorough descriptions.\n- The proposed loss functions are sensible and appear to contribute meaningfully to improving geometric accuracy.\n- The experimental section convincingly demonstrates the benefits of the proposed method on standard datasets.\n\n--- Weaknesses ---\n\nI did not identify any major flaws in the paper.\n\n- The most notable weakness is the significantly increased run-time compared to standard 3DGS. This is expected, given the complexity of the proposed losses. It would be valuable to provide an approximate run-time for each loss. For instance, according to Table 4, \nL_f  has only a marginal impact on geometric accuracy, so understanding its computational cost would help assess the trade-off.\n- The ablation study reports only geometric errors; including photometric errors would provide a more complete evaluation.\n- Figure 2 is not informative in its current form. It is unclear where the process begins or how the components interact. I recommend redrawing the figure to make the pipeline clearer.", "was_revised": true, "final_justification": "The authors addressed my concerns in the rebuttal.", "rebuttal": "### 1. The run-time cost of each loss.\n\n- We provide the runtime of each loss ablation on the TNT dataset in the table below.\nWith all losses enabled, the training takes 20.9 minutes on RTX 4090.\nRemoving $\\mathcal{L}_f$ (feature alignment) reduces training time to 15.94 minutes, yielding a ~5-minute saving, while still preserving strong performance.\nBy comparison, removing $\\mathcal{L}_p$ (photometric alignment) saves more time (8.3 min) but sacrifices more reconstruction quality.\nWhen only $\\mathcal{L}_I$ (image reconstruction) is used, our method has a lower time cost than the original 3DGS (5.9m vs. 7.5m) and provides better F1-score results than 3DGS (0.13 vs. 0.09).\n\n- We agree that $\\mathcal{L}_f$ brings modest improvements on the TNT dataset.\n$\\mathcal{L}_f$ improves general robustness and is beneficial in scenes with consistent lighting or texture.\nIt improves robustness under low-texture or lighting-variant scenes, which is not fully reflected by the F1-score metric alone.\nRemoving it often leads to over-smoothed meshes in challenging regions.\n\n- Moreover, the contribution of the feature alignment loss $\\mathcal{L}_f$ is more significant on the DTU dataset: removing $\\mathcal{L}_f$ increases Chamfer distance from 0.49 to 0.52.\nWe attribute this to dataset characteristics.\nDTU scenes benefit more from learned feature-level consistency due to cleaner lighting and smoother structure.\nTNT contains diverse indoor/outdoor scenes and severe lighting variation.\nThe expressive power of off-the-shelf image features is limited, which may partially underutilize $\\mathcal{L}_f$.\nWe plan to explore stronger feature extractors in future work.\n\n|                      |3DGS | Only $\\mathcal{L}_I$ | w/o $\\mathcal{L}_{nc}$ | w/o $\\mathcal{L}_{ns}$ | w/o $\\mathcal{L}_{p}$ | w/o $\\mathcal{L}_{f}$ | Full      |\n|:-:                   |:-:  |:-:                   |:-:                     |:-:                     |:-:                    |:-:                    |:-:        |\n|F1-score $\\uparrow$   |0.09 | 0.13                 | 0.52                   | 0.51                   | 0.50                  | 0.53                  | **0.54**  |\n|Time (m) $\\downarrow$ |7.5  | **5.9**              | 20.3                   | 17.1                   | 12.6                  | 15.9                  | 20.9      |\n|Time Gap              |-    | 15.0                 | 0.6                    | 3.8                    | 8.3                   | 5.0                   | 0.0       |\n\n\n### 2. Include photometric errors in ablation studies.\n\nThank you for the suggestion.\nAs shown in the table below, we provide additional photometric evaluation in our extended experiments.\nOn the TNT dataset, we compute image reconstruction errors (PSNR) for several ablation settings.\nThe results indicate that photometric differences across configurations follow a similar trend as geometric accuracy, and removing normal or alignment losses degrades image fidelity.\n\n|                     | w/o edge item | w/o $\\mathcal{L}_{nc}$ | w/o $\\mathcal{L}_{ns}$ | w/o $\\mathcal{L}_{p}$ | w/o $\\mathcal{L}_{f}$ | w/ scale loss | Full      |\n|:-:                  |:-:            |:-:                     |:-:                     |:-:                    |:-:                    |:-:            |:-:        |\n|F1-score $\\uparrow$  | 0.53          | 0.52                   | 0.51                   | 0.50                  | 0.53                  | 0.54          | **0.54**  |\n|PSNR $\\uparrow$      | 24.23         | 24.36                  | 24.38                  | 24.31                 | 24.16                 | 24.18         | **24.41** |\n\n\n\n### 3. Figure 2 is not informative in its current form.\n\nWe will redesign the figure to clearly indicate the data flow in the final version, including:\n- the starting point (posed images → Gaussian initialization),\n- how each loss module (e.g., $\\mathcal{L} _ I$, $\\mathcal{L} _ p$, $\\mathcal{L} _ f$) operates on intermediate outputs,\n- the sequential or parallel interactions among modules during training,\n- label key intermediate outputs (e.g., rendered normals, projected patches),\n- use arrows and modular blocks to better convey the pipeline structure."}, {"review_id": "npq4hlxeQf", "reviewer": "Reviewer_VnZx", "summary": "The authors address the problem of surface reconstruction from a 3D Gaussian Splatting scene. They introduce several losses using geometrical and multi-view cues to improve the reconstruction. The method achieves better reconstruction performance, albeit at a longer optimization time. The evaluation is conducted by comparing the surface reconstruction with a broad selection of related methods, including NeRF and Gaussian splatting approaches on several commonly accepted datasets.", "questions": "Could you explain how the recall in Table 4 is determined? Is there something classified (threshold of distance, which one was used)? While referring to previous work, it would make the paper more complete if you explained how you calculated it and why it matters.\n\nHow is the ablation in 5.2 done? Are the other terms rescaled, or is each term just dropped? Are the iterations the same?\n\nLine 221: “Additionally, due to lighting variations, the color of the same surface point may differ across views, making photometric consistency unreliable.” - Why is this a problem for geometry? Don't spherical harmonics address exactly this?", "limitations": "The primary limitation is the optimization time required by the method. One might question whether such a small gain in reconstruction quality is worth the resources spent on optimization.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strenghts**\nThe paper presents a clear mathematical formulation of the proposed loss terms, accompanied by a well-reasoned justification for how these additions enhance the geometric refinement of the Gaussians. The authors argue convincingly that the introduced terms address the limitations of purely photometric supervision by enforcing geometric consistency. The method is thoroughly evaluated through both quantitative and qualitative comparisons against a broad set of related approaches.\n\n**Weaknesses**\nThe paper suffers from several issues that affect its clarity and coherence. \n\nFor example:\n1. In Section 5.2, a scale loss is introduced in the ablation study without any prior mention or explanation in the method section. \n2. Placing result tables within the method section disrupts the logical flow of the paper and blurs the line between methodological description and experimental validation.\n\nIn terms of performance, the proposed method reports improved Chamfer distances in 9 out of 14 scenes on the DTU dataset, as shown in Table 1. However, these gains come at the cost of significantly longer training times - 15.5 minutes compared to 3.4 minutes for 3DGS, which diminishes the impact of the reported improvements, especially when they are relatively modest.\n\nMoreover, although the title emphasizes multi-view alignment, the actual performance gains attributed to multi-view supervision are minimal. The increase in Precision, Recall, and F1-score when scaling from a single view (N=1) to four views (N=4) is only +0.02, which raises doubts about the effectiveness of the method’s multi-view strategy and the strength of its central claim.", "was_revised": true, "final_justification": "The paper presents a clear formulation of the proposed loss terms, accompanied by a well-reasoned justification for how these additions improve the geometric refinement of the Gaussians. The authors argue convincingly that the introduced terms address the limitations of purely photometric supervision by enforcing geometric consistency. The method is thoroughly evaluated through both quantitative and qualitative comparisons against a broad set of related approaches. The authors adequately addressed all the questions raised during the review.", "rebuttal": "### 1. The scale loss in Section 5.2 is introduced without any prior mention.\n\nIn Lines 131–135 of Section 3 (“Normal and Depth Estimation from Gaussians”), we describe how the Gaussian scale matrix $\\boldsymbol{S}$ encodes ellipsoid shape, and how its smallest eigenvalue determines the normal vector (\"$\\boldsymbol{S}$ encodes the scale along these directions......the smallest scale factor as the normal $\\boldsymbol{n}$ of the Gaussian.\").\nHowever, we agree that the scale loss itself was not explicitly defined, which may affect clarity.\n\nThe scale loss is a common regularization strategy in recent 3D Gaussian-based methods, often employed to encourage ellipsoids to flatten into local planes, thereby improving the estimation of surface normals and tangent geometry.\nOur purpose in including this loss in the ablation study (Section 5.2) was to show that, unlike in prior work, this commonly used regularization is unnecessary in our framework, due to the effectiveness of our view-alignment and geometry-aware supervision.\nIn other words, the added flattening has no additional benefit.\nWe will add a formal description of this loss and clarify its context in the final version.\n\n\n### 2. Tables placed in the method section disrupt logical flow.\n\nThank you for the helpful suggestion.\nIn the revised version, we will relocate these tables to the experiment section to improve logical flow and distinguish between method description and validation.\n\n\n### 3. The performance gains come at the cost of significantly longer training time.\n\n- While our training time is longer than that of vanilla 3DGS, the added cost stems from the newly introduced geometry supervision and view-alignment mechanisms, which directly improve surface accuracy.\nThese components are essential for multi-view reconstruction, especially in challenging scenes.\nWhile the gains over the latest baselines may appear modest in some scenes, these differences represent noticeable geometric improvements.\n\n- Moreover, increased time is a common trade-off among geometry-enhanced 3DGS variants, where introducing geometric priors and view constraints improves reconstruction quality at the cost of runtime (see Tables 1 & 2).\nThe original 3DGS remains the fastest due to its lightweight constraints, but also exhibits the least accurate geometry.\nIn future work, we plan to explore adaptive Gaussian pruning to further reduce training cost without sacrificing accuracy.\n\n- Our goal of this work is not to accelerate or compress 3DGS, but to enhance its geometric representation.\nOn DTU, we reduce Chamfer distance from 1.96 to 0.49; on TNT, we raise F1-score from 0.09 to 0.54, a substantial improvement over 3DGS; on Mip-NeRF 360, we also observe consistent gains across all metrics.\nAs shown in the table below, when only using edge-aware image reconstruction loss $\\mathcal{L}_I$ on TNT, our method runs faster than 3DGS (5.9 min vs. 7.5 min) and achieves a higher F1-score (0.13 vs. 0.09).\n\n- In terms of practicality, our training time on TNT (~20 min) is production-viable for many offline applications.\nIn contrast, neural implicit SDF methods often require 10+ hours of training (see Tables 1 & 2).\nWe believe that accuracy is often prioritized over marginal runtime gains in such scenarios, with the rapid advancement of hardware.\n\n|                       |3DGS | Ours (only $\\mathcal{L}_I$) |\n|:-:                    |:-:  |:-:                          |\n|F1-score $\\uparrow$    |0.09 | **0.13**                    |\n|Time (m) $\\downarrow$  |7.5  | **5.9**                     |\n\n\n### 4. Ablation from N=1 to N=4 casts doubt on the effectiveness of the multi-view strategy.\n\nWe believe this is based on a misunderstanding of the ablation experiment in Section 5.2.\nSpecifically, N denotes the number of source views, not the total number of images used.\nAs stated in Line 192, our framework takes both a reference view and N source views.\nThus, N=1 still uses 2 input images and does not correspond to a single-view setup.\n\nTo directly evaluate the effectiveness of the multi-view strategy, we conducted an additional ablation by removing all multi-view-based losses: the photometric alignment loss $\\mathcal{L} _ {p}$ and feature alignment loss $\\mathcal{L} _ {f}$ (Eqs. 7 and 12), while retaining only single-view terms: image reconstruction $\\mathcal{L} _ {I}$, normal consistency $\\mathcal{L} _ {nc}$, and normal smoothing $\\mathcal{L} _ {ns}$.\nAs shown in the table below, the average F1-score on TNT drops from 0.54 to 0.36, confirming that our multi-view strategy plays a critical role.\n\nFinally, we emphasize that our central contribution includes not only multi-view alignment but also single-view alignment (edge-aware image loss and normal-based supervision).\nAs shown in Table 4, each component contributes meaningfully to the overall performance.\n\n|                                             | Precision $\\uparrow$  | Recall $\\uparrow$ | F1-score $\\uparrow$ |\n|:-:                                          |:-:                    |:-:                |:-:                  |\n|w/o $\\mathcal{L} _ {p} + \\mathcal{L} _ {f}$  | 0.33                  | 0.40              | 0.36                |\n|Ours                                         | **0.51**              | **0.60**          | **0.54**            |\n\n\n### 5. Explanation of how the recall in Table 4 is determined.\n\nThe results in Table 4 are based on the Tanks & Temples (TNT) benchmark, and we follow the official evaluation protocol and code provided by the dataset authors, which is publicly available on GitHub.\nThe precision, recall, and F1-score are computed as follows.\n- Let $\\mathcal{G}$ be the ground‑truth point set and $\\mathcal{R}$ the reconstructed points.\n- For each ground‑truth point $g \\in \\mathcal{G}$, we compute $e_{g \\to \\mathcal{R}} = \\min_{r \\in \\mathcal{R}} \\|g - r\\|$.\n- Given a threshold $d$, recall is defined as: $R(d) = \\frac{100}{|\\mathcal{G}|} \\sum_{g \\in \\mathcal{G}} [e_{g \\to \\mathcal{R}} < d]$.\n- Precision $P(d)$ is defined similarly by measuring distance from reconstructed points to ground-truth. The F1-score is their harmonic mean: $F(d) = \\frac{2 P(d) R(d)}{P(d)+R(d)}$.\n\nThe distance threshold $d$ defines a successful match between reconstruction and ground truth.\nImportantly, the distance threshold is not a fixed value, it is defined per-scene within the TNT evaluation code, based on each scene’s scale and ground-truth annotations.\n\nAlthough prior works on TNT typically report only the F1-score, which is the harmonic mean of precision and recall, we additionally report precision and recall separately in Table 4 to provide a more comprehensive ablation study.\nThis helps assess the impact of each component (e.g., loss terms, view counts) on reconstruction accuracy (precision) and completeness (recall).\nWe will clarify this detail in the revised version for completeness.\n\n\n### 6. More explanation of the ablations in Section 5.2.\n\nIn all ablation studies reported in Section 5.2, we adopt a consistent and controlled protocol to ensure fair comparisons:\n- When ablating a loss term (e.g., $\\mathcal{L} _ {p}$ or $\\mathcal{L} _ {f}$), we drop the corresponding term entirely from the optimization objective.\n- The remaining loss weights are kept unchanged, and we do not re-normalize or re-scale other terms to preserve consistent training dynamics.\n- All experiments are conducted with the same number of training iterations and identical optimization settings (e.g., learning rate, batch size, data split).\n- For the hyperparameter ablations, we similarly vary only the parameter under study (e.g., source view count, threshold value), while keeping all other parameters and training settings fixed.\n\nThis ensures that any performance change can be attributed directly to the presence or absence of the specific loss being tested.\nWe will clarify these implementation details in the revised version.\n\n\n### 7. Why is the lighting variation a problem for geometry if spherical harmonics model illumination?\n\nAlthough spherical harmonics (SH) can approximate lighting variations in many scenarios, their reconstruction is inherently low-frequency—suitable for modeling large-scale diffuse illumination but insufficient for high-frequency variations such as specular highlights, cast shadows, and fine-scale reflectance changes.\nGeometry inference based solely on photometric consistency requires accurate alignment of surface appearance across views.\nHowever, when lighting varies (due to different exposures, shadowing, or specularity), the corresponding pixel intensities may diverge even when geometry is correct.\nThis breaks the photometric consistency assumptions common in MVS pipelines, degrading normal and depth estimation accuracy.\n\nMoreover, SH assumes Lambertian, low-frequency lighting and cannot recover sharp contrast variations.\nIn uncontrolled multi-view datasets, lighting conditions may vary significantly, and SH-based alignment often fails to support robust geometric supervision.\nTherefore, while SH is useful in graphics/rendering pipelines, it does not guarantee photometric invariance for geometry reconstruction in real-world multi-view imagery.\nOur method relies on view alignment and multi-view geometric cues (edge-aware, normal-based, and feature alignment supervision) to remain robust to photometric inconsistency."}, {"review_id": "hpup0oAzME", "reviewer": "Reviewer_78eD", "summary": "This paper proposes a new method for 3DGS-based surface reconstruction. It enforces multi-view color and feature consistency, as well as image gradient-guided local normal consistency, normal smoothness. The results show that the proposed method can achieve better performance on both reconstruction and rendering.", "questions": "1. What's the difference between the proposed Multi-View Photometric Alignment and the one in PGSR? An ablation study to illustrate the difference would be helpful.\n2. In Equation 4, an ablation study to evaluate the effectiveness of the edge-aware coefficient is needed, since it's the main difference from the existing methods.", "limitations": "Please see the weaknesses and questions above.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 1, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. The novel contribution in this paper is mainly the multi-view feature-level consistency, which learns from the MVS pipeline to align the high-dimensional features.\n\nWeaknesses:\n1. Significance: The results show no significant performance improvement over the previous state-of-the-art PGSR. According to Table 4, the proposed feature alignment only improves F1 Score by 0.01, and without which the performance becomes similar to PGSR. Therefore, I struggle to find the real significance of this work.", "was_revised": true, "final_justification": "The authors showed more detailed results and I think overall this paper provides some further improvements over PGSR.", "rebuttal": "### 1. Concern about the significance and no significant performance improvement over PGSR.\n\n- Firstly, we would like to clarify that our method is not a direct extension of PGSR with feature alignment added on top.\nInstead, our framework introduces a new combination of single-view alignment (including edge-aware image reconstruction and normal-based geometry supervision) and multi-view alignment (photometric and feature alignment), which are structurally and conceptually different from PGSR.\n\n- As shown in the ablations of Table 4, even without feature alignment, our method already achieves better performance than PGSR.\nThis demonstrates that our core design, particularly single-view and multi-view photometric supervision, is effective.\nThe additional feature alignment further improves the results, validating our design choices.\nEven when the improvement appears numerically small on the TNT dataset, reaching the same or slightly higher performance than PGSR still represents a state-of-the-art level.\n\n- Moreover, on the DTU dataset, the contribution of the feature alignment loss $\\mathcal{L}_f$ is more significant.\nAs shown in the following table, removing $\\mathcal{L}_f$ increases Chamfer distance from 0.49 to 0.52, which is still better than PGSR’s reported 0.53.\nThis shows that our method offers consistent geometric improvement across datasets.\n\n- Finally, our method also outperforms PGSR on the Mip-NeRF 360 dataset for novel view synthesis, as shown in Table 3.\nWe achieve better scores on all metrics, indicating that our method is not merely comparable to PGSR but surpasses it in reconstruction/synthesis quality and generalizability across varied benchmarks.\nThe ablation experiment in *Question 2* also shows that the strategy in our method can provide better performance.\n\n|                              | w/o $\\mathcal{L}_{f}$ | Ours     | PGSR   |\n|:-:                           |:-:                    |:-:       |:-:     |\n|Chamfer distance $\\downarrow$ | 0.52                  | **0.49** | 0.53   |\n\n\n\n### 2. The difference between the proposed Multi-View Photometric Alignment and that in PGSR.\n\nOur Multi-View Photometric Alignment differs from PGSR’s in several key aspects:\n\n- Optimization complexity: PGSR couples its photometric consistency loss with geometric consistency regularization, which minimizes forward–backward reprojection error of neighboring views, resulting in more variables participating in network backpropagation and a more complex gradient computation.\nIn contrast, our method simplifies the backpropagation path by aligning Gaussian orientations first and then applying image/feature-level constraints, which leads to better optimization efficiency.\n\n- Multi-view formulation: PGSR computes photometric consistency between one reference view and one source view.\nOur framework uses one reference view and multiple source views (three by default, varied in ablation), which introduces richer multi-view constraints and improves geometric supervision.\n\n- Gaussian flattening: PGSR flattens 3D Gaussians using scale regularization of the Gaussian ellipsoid to mimic planar surfaces.\nIn our method, we found this strategy ineffective through ablation in Table 4, and therefore designed our supervision differently.\n\n- Efficiency and performance: As shown in Table 2, our method achieves better reconstruction quality across more scenes on real outdoor scenes of the TNT dataset while requiring less training time than PGSR.\n\n- Occlusion modeling: We explicitly define a visibility term and occlusion weight to mitigate the effect of outlier pixels caused by occlusion or misalignment.\nWe give their motivation and clearly show the derivation of their formulas in the method section.\n\nTo further clarify the differences, we provide a cross-method ablation comparison on the DTU and TNT datasets in the following table.\nThese results show that our alignment strategy is both more effective and more efficient than PGSR’s when integrated into either framework.\n“PGSR+Ours” means adding our solution to PGSR and vice versa.\n\n|                                     | PGSR   | PGSR+Ours | Ours      | Ours+PGSR |\n|:-                                   |:-:     |:-:        |:-:        |:-:        |\n|Chamfer distance on DTU $\\downarrow$ |0.53    |0.51       |**0.49**   |0.56       |\n|F1-score on TNT $\\uparrow$           |0.52    |0.53       |**0.54**   |0.48       |\n|Time on TNT (minutes) $\\downarrow$   |25.5    |19.7       |20.8       |22.1       |\n\n\n\n### 3. Add an ablation study of the edge-aware coefficient in Equation 4.\n\nThank you for the helpful suggestion.\nTo evaluate its impact, we conducted an ablation study where we removed this coefficient and kept the rest of the loss formulation unchanged.\nThe table below shows that the edge-aware coefficient improves reconstruction performance.\nIn boundary regions, Gaussian primitives often exhibit ambiguous or noisy normal directions, which can lead to incorrect supervision signals.\nThe coefficient reduces the loss contribution from these areas, allowing the network to focus learning on more reliable surface regions.\nWhile the improvement is modest, it reflects the fact that shape boundaries constitute a relatively small proportion of the scene, and thus affect only a small number of sampled points during evaluation.\n\n|                          | Precision $\\uparrow$  | Recall $\\uparrow$ | F1-score $\\uparrow$ |\n|:-:                       |:-:                    |:-:                |:-:                  |\n|w/o edge-aware weighting  | 0.50                  | 0.59              | 0.53                |\n|Ours                      | **0.51**              | **0.60**          | **0.54**            |"}]

Author rebuttal:

Meta-review: In this paper, the authors presented a new method for 3D Gaussian Splatting (3DGS) by view alignment to enhance the geometric representation. Specifically, edge-aware image cues, photometric alignment, and cross-view consistency are incorporated to enhance the geometric representations. Experiments on standard benchmark datasets show the effectiveness of the proposed method.

This paper received review comments from four expert reviewers,with an initial divergent rating but reached a consensus after the rebuttal: 1 Accept and 3 Borderline Accept. The reviewers appreciate the motivation and idea, as well as the strong performance. Although there were some concerns about the novelty, missing comparison to related work, and some clarity issues, after the rebuttal and discussion phase, the major concerns were addressed. As a result, the AC is pleased to recommend Accept. But the authors are advised to incorporate all the further clarifications during the discussions and rebuttal into their final version.
Decision: Accept (poster)