SurfSplat: Conquering Feedforward 2D Gaussian Splatting with Surface Continuity Priors
Authors: Bing He
Jingnan Gao
Yunuo Chen
Ning Cao
Gang Chen
Zhengxue Cheng
Li Song
Wenjun Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: 3D Gaussian splatting
2DGS
feed forward reconstruction
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: Explicitly combine the feedforward reconstruction with surface continuity priors, significantly improve the geometric performance of generated assets.

Abstract: Reconstructing 3D scenes from sparse images remains a challenging task due to the difficulty of recovering accurate geometry and texture without optimization.
Recent approaches leverage generalizable models to generate 3D scenes using 3D Gaussian Splatting (3DGS) primitive. 
However, they often fail to produce continuous surfaces and instead yield discrete, color-biased point clouds that appear plausible at normal resolution but reveal severe artifacts under close-up views. 
To address this issue, we present SurfSplat, a feedforward framework based on 2D Gaussian Splatting (2DGS) primitive, which provides stronger anisotropy and higher geometric precision. By incorporating a surface continuity prior and a forced alpha blending strategy, SurfSplat reconstructs coherent geometry together with faithful textures.
Furthermore, we introduce High-Resolution Rendering Consistency (HRRC), a new evaluation metric designed to evaluate high-resolution reconstruction quality. Extensive experiments on RealEstate10K, DL3DV, and ScanNet demonstrate that SurfSplat consistently outperforms prior methods on both standard metrics and HRRC, establishing a robust solution for high-fidelity 3D reconstruction from sparse inputs.

Published as a conference paper at ICLR 2026
SURFSPLAT: CONQUERING FEEDFORWARD 2D GAUS- SIAN SPLATTING WITH SURFACE CONTINUITY PRIORS
Bing He & Jingnan Gao & Yunuo Chen
& Gang Chen 2 & Ning Cao 2 & Zhengxue Cheng
& Li Song & Wenjun Zhang'
Shanghai Jiao Tong University,
2 Tianyi Shilian Technology Co., Ltd
(sandwi cherchotetthmmmmmmooooomoooo 8 jsuu.. ettjt...uuu.o edu. cn
Reference
SurfSplat
Overview
Continuity
Prior
Realistic
surfaces
2DGS Primitive
Forced a-Blending
DepthSplat
MVSplat
pixelSplat
pixelSplat
MVSplat
DepthSplat
SurfSplat
Figure 1: SurfSplat feedforward network that predicts a 3D scene representation from sparse images input. Previous methods often produce sparse, color-biased pointclouds that lack surface continuity, especially under close-up views. In contrast, our SurfSplat approach utilizes 2DGS with surface continuity prior and forced alpha blending to generate coherent and realistic 3D surfaces.
ABSTRACT
Reconstructing 3D scenes from sparse images remains a challenging task due to the difficulty of recovering accurate geometry and texture without optimization. Recent approaches leverage generalizable models to generate 3D scenes using 3D Gaussian Splatting (3DGS) primitive. However, they often fail to produce continuous surfaces and instead yield discrete, color-biased point clouds that appear plausible at normal resolution but reveal severe artifacts under close-up views. To address this issue, we present SurfSplat, a feedforward framework based on 2D Gaussian Splatting (2DGS) primitive, which provides stronger anisotropy and higher geometric precision. By incorporating surface continuity prior and a forced alpha blending strategy, SuríSplat reconstructs coherent geometry together with faithful textures. Furthermore, Consistency (HRRC), a new evaluation metric designed to evaluate high-resolution reconstruction quality. Extensive experiments RealEstate 10K, DL3DV, and ScanNet demonstrate that SurfSplat consistently outperforms prior methods on both standard metrics and HRRC, establishing robust solution for high-fidelity
introduce High-Resolutton Rendering

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Published as a conference paper at ICLR 2026
3D reconstruction from sparse inputs. Project page: https::/hebinq-s// wtt..auspptaatttteebbee.ee
INTRODUCTION
Reconstructing geometrically accurate real-world scenes continues to be a longstanding challenge in 3D vision. Such capability is crucial for applications like immersive VR experiences, realistic gaming environments, and digital content creation, where both geometric fidelity and visual consistency are essential. To address this, 3D Gaussian Splatting (3DGS) Kerbl et al. (2023) has recently shown impressive performance in novel view synthesis and scene reconstruction. It represents a scene as collection of discrete, semi-transparent ellipsoids, which are rendered onto the image plane through "splatting". Existing Gaussian-basee reconstruction methods mainly follow two paradigms. Traditional approaches, such vanilla 3DGS, rely on preprocessing step using COLMAP Schönberger & Frahm (2016) to generate an initial point cloud and typically require access to hundreds of posed views. These methods then perform scene-specific optimization over tens of thousands of iterations, often taking several hours to converge to high-quality results. In contrast, feedforward approaches employ pretrained models directly predict per-pixel 3D Gaussians from sparse inputs-often as few as two images- any preprocessing. These methods can reconstruct 3D scene within milliscondss, enabling real-time and scalable applications.
However, we observe that existing feedforward methods tend to generate degraded 3D scenes. The reconstructed surfaces often collapse into nearly spherical, discrete point clouds with color biases and visible voids. This degradation stems from the under-utilization of the anisotropic properties of Gaussian primitives, which makes it difficult to disentangle geometry from appearance. Moreover, since current feedforward methods rely primarily on image loss, they often yield biased geometry and appearance under sparse or weakly constrained viewpoints. These issues are often subtle in rendered images at the original resolution and near reference views, but become prominent when the camera moves closer or shifts to off-axis viewpoints. This discrepancy indicates that standard novel view synthesis (NVS) metries fail to accurately capture the geometric and textural fidelity of the scene. To address these challenges and provide a more accurate reconstruction, we propose SurfSplat, a feedforward model that reconstructs 3D scenes from sparse images using 2D Gaussian Splatting (2DGS) as the representation primitive. Unlike 3DGS, 2DGS captures anisotropic structures more effectively, resulting in improved geometric precision. However, direct training of 2DGS often suffers from instability that arises from the complex coupling between geometric attributes and rendering outcomes. This issue is amplified under limited supervision, where gradients cannot effectively disentangling geometry from appearance. The faceted nature of 2D Gaussians further intensifies the problem, as even minor geometric perturbations can produce substantial deviations in rendered outputs. To tackle this, we introduce two key components: (1) an explicit surface continuity prior, which binds the rotation and scale attributes of each 2DGS to its spatial position, encouraging smooth and coherent surfaces. (2) forced alpha blending strategy, which helps the model escape local optima and reduces color bias during training.
Evaluating the quality of 3D scenes produced by feedforward models is also nontrivial. Traditional geometry metrics such as Chamfer Distance or F1 Score are ineffective due to incomplete or sparse outputs and the lack of dense ground truth. Furthermore, most datasets lack oft-o--distribution viewpoints for reliable assessment. To address this, we propose High-Resollttin Rendering Consistency (HRRC): a novel metric that evaluates scene fidelity by rendering the 3D model at high resolutions, thereby simulating close-up views that expose hidden artifacts like spatial voids. Moreover, HRRC can be computed directly from standard datasets without requiring new annotations. Built upon these components, SurfSplat reconstructs continuous, high-fidelity 3D scenes with sig- nificantly fewer holes and artifacts when viewed from challenging perspectives. Unlike previous 3DGS- based methods that predict Gaussian attributes independently. our approach explicitly models continuity and structure, enhancing both geometric accuracy and rendering consistency. In summary, the main contributions of this work are follows:
We propose SurlSplat, a feedforward network that reconstructs 3D scenes using 2D Gaus- sian surfels from sparse inputs. Our model leverages a surface continuity prior and forced alpha blending to significantly improve reconstruction quality.
We introduce HRRC, a high-resolution rendering- based metric that reveals surface disconti- nuities and enables fairer evaluation of -eward-generrted scenes through dense sampling.
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Extensive experiments demonstrate that SurfSplat achieves state-of-the-art performance in both standard and HRRC metrics on RealEstate 10K, DL3DV, and ScanNet, setting a new benchmark for novel view synthesis under sparse-view settings.
RELATED WORKS
2.1 3D GAUSSIAN SPLATTING
Recent Neural Radiance Field (NeRF) Mildenhall et al. (2021) approach has proven effective for scene reconstruction by leveraging a continuous implicit representation of the scene. Subsequent works have improved reconstruction quality by evolving from MLPs to grid-based structures. For instance, Müller et al. (2022) introduced the Instant Neural Graphics Primitives (Instant-NGP), while Fridovich-Keil et al. (2022) proposed Plenoxels. Other methods, such as Mip-NeRF Barron et al.
[ model rays as cones achieve anti-aliasing.
To accelerate rendering, various strategies have heen explored, including precomputation Wang et al. al. (2022); Yu et al. (202T) and hash-based encoding Müller et al. (0022) Takikawa et al. (2022). Additionally, several extensions have adapted NeRF to dynamic
(2023 (022):Fridovicch
scenes Xian et al. allaa.2200) (2021) Park et al. (2021a a b); Pumarola al. (2021) Song et al. (2023).
More recently. 3D Gaussian Splatting KerGS) et al. (2023) introduced an efficient, point-based
rendering approach. By representing scenes as collections of semi- transparnnn,, anisotropic Gaussians in 3D space, 3DGS enables photorealistic rendering via rasterization-base splatting.
Numerous extensions have emerged to enhance the capabilities of 3DGS, targeting various aspects
such as: optimization efficiency Cheng al. (2024); Zhang al. (2024); Radl et al. (2024); Diolatzis ctt al. (2024), anti-aliasing Yan et al. (2024): Yu et al. (2024); Song et al. (2024); Liang et al. (2024).
geometric fidelity |Huang al. [ (2024), and representation compression for faster inference Girish
et al. (2024); Navaneet et al. (2024); Niedermayr et al. (2024); Lee et al. (2024); Fan et al. (2024);
Chen et 2024a). Efforts extend 3DGS to dynamic scenes have also been explored Luiten et al. (2023): Wu et al. y Wan et al. (2024); Huang et al. (2023); He et al. (2024).
Luiten
(2024) proposed 2DGS, novel differentiable surface element capable of representing surfaces with higher accuracy. However, conventional 3DGS pipelines typically require precomputed sparse point clouds, accurate camera poses, and extensive per-scene optimization, limiting their applicability in sparse-view settings.
Among these, Huang
2.2 GENERALIZABLE 3D RECONSTRUCTION
To alleviate the need for costly per-scene optimization, recent works explored feedforward networks that directly predict 3D Gaussians from sparse image collections.
Splatter image Szymanowicz et al. (2024) proposed novel paradigm for converting images into Gaussian attribute images. Other approaches incorporated task-specific backbones to improve reconstruction by leveraging geometric cues. For example, PixelSplat Charatan et al. a (2024) used epipolar geometry for efficient depth estimation, while MVSplat Chen et al. (2024b) builded cost volumes to aggregate multi-view information.
Follow-up works further extended these ideas. FreeSplat Wang et al. (2024b) addressed limited synthesis range via pixel-wise triplet fusion strategy. Hisplat lang et al. (024) predicted multiple
Gaussian layers in hierarchical structure. DepthSplat(Xu al. (024b) enabled cross cross-task interaction between depth estimation and Gaussian splatting.
Several researches also focused improving generalization by introducing triplane representa- tions Zou et al. )2024); Xu et al. (2024a). SplatFormer (Cen et al. (2024d) leveraged pretrained models to improve performance out-of -idi-ii--dttttttioon views. NopoSplat Ye al. (2024) abandoned the transform-ther fuse pipeline and directly generated 3D scenes in canonical space. G3R Chen al. (2024c) extended the generalizable 3DGS dynamic scenes using auxiliary LiDAR data. Despite these advancements, prior feedforward methods primarily rely on 3DGS primitives. Without effective regularization, the generated 3D scenes often lack realistic and continuous surfaces. These degradations are typically unseen at original resolution near reference views, but become apparent under close-up or off-axis inspection.
In contrast, our approach adopts 2DGS the scene representation primitive. By introducing surface continuity prior and forced alpha blending technique, our model successfully trains highly anisotropic surface elements, enabling high-fidelity 3D scene reconstruction from sparse inputs.
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3 METHOD
3.1 PRELIMINARIES
Feedforward 3D Gaussian Splatting (3DGS) methods aim to regress a set of 3D Gaussians di- rectly from sparse multi-view images. Unlike aptimizatiionbasse approaches that iteratively refine Gaussians, feedforward methods predict all Gaussian parameters in a single forward pass. Given collection of input images [1') with corresponding camera intrinsics (k") (T* Je=1, the network fe predicts Gaussian parameters for each pixel as:
Ju=I and poses
Hxw U
a)) U
So (I" T (T",,"
where μi denotes the 3D position, Or the opacity, ry the rotation, sy the scale, and oj the spherical harmonics of the j-th Gaussian generated from the v-th view. The feasibility of such models arises from the observation that, even with sparse-view conditions, image features extracted by modern backbones (e.g., ViT' et al. (202T): /Zhang et al. (002)); Wang et al. (2024a)) retain sufficient local geometric cues for direct 3D reasoning. When combined with the camera intrinsics, these features can be projected into 3D space and assigned accurate Gaussian attributes, enabling end-to-end training via differentiable rasterization and photometric reconstruction loss.
3.2 MODEL ARCHITECTURE
Continuity Prior
Depth
Multi-view
U-Net
Pixel Surface
single-view
Appearance
Forced
blending
Encoder
GS Processor
SurfSplat
Figure 2: Illustration for model architecture. Given sparse input images, our dual-patl encoder processes them through both single-view and multi-view branches. The fused features are passed through U-Net to predict intermediate attributes, including depth, scale multipliers, and appearance components. Finally, these intermediates are converted into standard Gaussian attributes using our surface continuity prior and forced alpha blending strategy.
In the context of feedforward 3D Gaussian Splatting (3DGS), multi-view cues are essential for enforcing geometric consistency across views, while single-view priors offer guidance in regions with missing textures or insufficient correspondences. To integrate these complementary sources effectively, we adopt dual-path for feature extraction within our model architecture. In the single- view branch, we leverage a pretrained monocular depth backbone. Specifically, we use the Depth Anything V2 model Yang al. (2024), and bilinearly upsample its output features to the target spatial resolution. In the multi-view branch, input images are first converted into low-resolution feature maps, which are then processed by multiple layers of self- and cross-attention Vaswani al. (2017 7): Liu et al. (b021b) to extract inter-view correspondences. The fused features are subsequently used to construct cost volumes Chen et al.) (2024b) across views via the plane-sweep stereo approach Collins (199);; Xu et al. (2023), which serve as the output of the multi-view branch. The final feature representation obtained by concatenating the single-view and multi-vies features.
The combined feature is fed into a 2D U-Net Ronneberger et al.] (2015); |Rombach et al. (2022) to regress the Gaussian Splatting (GS) attributes, including depth, scale multipliers, higher-order spherical harmonics components, and opacity. These outputs are upsampled to full resolution using a DPT head Ranftl et al. (202I) and further processed with our surface continuity prior and forced alpha-blending techniques to produce the final standard Gaussian attributes. Technical details are provided in Appendix A.I
3.3 SURFACE CONTINUITY PRIOR
Existing feedforward 3DGS methods often produce incoherent and discontinuous surfaces. This stems from the fact that learnable Gaussian primitives struggle decouple geometry and texture attributes
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when trained solely through gradient-basee based supervision. A closer inspection of rendered results reveals biased color assignments, surface discontinuities, and voids. While these primitives may collectively produce visually plausible images under common rendering settings, the underlying 3D assets remain structurally flawed and fall short the fidelity required for high-quality 3D generation. To address these issues, start by observation: most visible geometry in real-world scenes consists of smooth, continuous surfaces. This motivates the introduction of surface continuity prior, which assumes that spatially adjacent surfels on a coherent 3D surface generally correspond to neighboring pixels in the image. Guided by this prior, Gaussians are expected to exhibit correlated geometric attributes. Specifically, the rotation and scale of each Gaussian should be strongly aligned with the positions of its neighboring Gaussians. We consider the image-space neighborhood around pixel at (hi, w), whose associated Gaussian has a 3D position Po E RR$ with neighboring positions (p.) Following the standard COLMAP coordinate convention, where the camera frame has pointing right. y downward, and z inward, we assume that the default (unrotated) surface normal aligns with the canonical vector no (0,0, 0, 1) The initial rotation Ro SO(3) is set to the identity matrix, which corresponds to the quaternion (1,0,0, 0,0).
To estimate the local surface orientation, apply rightward and downward Sobel filters over the 3 x 3 neighborhood around po. obtaining two virtual neighbors, P1 and p2. These neighbors define two tangent vectors:
t1, t2 P1 Po. P2 Po-
(2)
Although ti and are not guaranteed to be orthogonal in world space, their projections onto the image plane are orthogonal. The local surface normal E I& is then computed as their cross product:
t x t2
|t| x t2ll
Given this target normal n, the corresponding rotation matrix R E SO(3) that aligns no with n can be computed using Rodrigues' rotation formula:
e
R = I+ Mx
+
|v/
where v = no c = Do n, and [v]x denotes the skew-symmetric matrix of v. This rotation aligns the canonical frame with the estimated local surface, giving the updated surfel rotation:
R uar R.R R.
(5)
To define anisotropic scale S diag(o., da ow). we compute the variance of projected neighboring points along the rotated tangent axes tus tp. Since we employ 2D Gaussian splats, the scale along the depth axis a is fixed to zero. To account for screen-space deformation, let W R4x4 denote the transformation ma- trix from world space to screen space, and let J represent the Jacobian of the affine approximation of the projective transformation:
Neighboring 2D pixels Pixelwise 3D Coordinates
Neighboring 2D Gaussians
Continuous Surface
Sobel
Filter
Surface
f(
Normal
Scale Og Cy
Figure 3: Illustration for Gaussian processor. We visualize how image-space neighboring pixels are transformed into Gaussians aligned on continuous surface via the surface continuity prior. To prevent opacity collapse and preserve 3D alignment, we apply a forced alpha-blending strategy that reduces opacities, ensuring that spatially occluded Gaussians still contribute during rendering.
Σ RSS R
(6)
E' JWEW
(7)
where E' corresponds to a unit circle in the image plane, as in feedforward methods each GS corresponds one-to-one with an image pixel and its projection always covers single pixel. However, inverting the projection matrix to estimate scale often yields unstable values that hinder convergence. To address this, we adopt a coarse scale estimate based on image-space distances between neighboring pixels:
a2 o6 =t? tig
tz + 12
(8)
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We then use the neural network to predict scale multipliers o. ou, which are constrained to lie within 3 3] The final scales are then computed as:
TU Ouou: 0, = 0.
With this design, instead of directly regressing Gaussian attributes, our method derives them from predicted 3D positions, guided by physically grounded constraint to ensure spatial consistency. This formulation provides geometry-awan initialization of 2D Gaussian splats in 3D space, ensuring that their orientation and shape remain consistent with surface continuity.
3.4 FORCED ALPHA BLENDING
While the surface continuity prior imposes effective local geometric constraints for continuous 3D reconstruction, we observe that can lead to suboptimal local minima during training. Specifically, the model tends to learn highly opaque Gaussians, where individual splats saturate the pixel opacity. This behavior rapidly boosts image quality for near-input viewpoints, but under the Siphabblending rendering rule, occluded Gaussians contribute minimally to the output:
C= Ga, II), VEN
a= -a (
I
(10)
j=1
SEN ==1
As result, deeper Gaussians in the rendering order effectively ignored, which impairs the model's ability to learn 3D structure and maintain alignment.
To address this, we propose a foreed alpha blending strategy that explicitly constrains each Gaussian's opacity. We clip the predicted opacity using upper bound Topu < 1, ensuring that all Gaussians contribute to the rendering regardless of their depth order. This preserves both the model's multi-layer expressiveness and its 3D alignment capabilities. To further improve the reliability of spherical harmonics -base color estimation under enforced blending, we apply two adjustments. First, we initialize the RGB color directly into the DC component of the SH basis. Second, We normalize the rendered output to compensate for transparency, since the final alpha holds a < by design:
C.
< To:
C
(1I)
To:
where Ta is stability threshold to avoid amplifying noise in regions with very low transparency. This correction allows the model produce unbiased and stable renderings, while maintaining accurate 3D alignment in vierse-view scenarios.
3.5 TRAINING LOss
Our training loss is an image-level loss computed directly between the rendered image and the ground-truth image. We use combination of mean squared error (MSE) and perceptual similarity (LPIPS):
(MSE (Fender: 1
+ LPIPS (Prender: 1g)))
(12)
m==1
where M denotes the batch size. The weight set to 0.05, following prior works Charatan et al. (2024): Chen et al. (2024b); Xu et al. (2024b).
3.6 HIGH-REE ReSOLUTIOO RENDERING CONSISTENCY (HRRC)
To better evaluate the geometric fidelity of reconstructed 3D scenes, we propose novel evaluation metric: High-Resolution Rendering Consistency (HRRC).
Conventional metrics- such as PSNR, SSIM, and LPIPS are typically computed at the same resolution as the input images (e.g., 256 256). However, these metrics often fail reveal geometric inaccuracies or sparsity-induced artifacts, which may be hidden at lower resolutions but become apparent under eigh-frequency sampling.
To address this limitation, render each reconstructed scene at higher resolution (c.g., 2x or 4x the original), resulting in output f We compare this against bicubic- sesublld version of the ground truth image, denoted jGTt, and compute standard quality metrics:

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HRRCmettii = metric( jaT)) where metric E (PSNR, SSTM, LPIPS).
(13)
HRRC can effectively expose geometric flaws such as sparsity-induced holes, degenerate Gaussian shapes (e.g., overly isotropic splats), and discontinuities in unobserved regions. A higher HRRC score indicates stronger spatial generalization and more accurate 3D reconstruction. This makes HRRC particularly useful for distinguishing models that merely memorize sparse views from those that truly recover 3D geometry.
4 EXPERIMENT
pixelSplat
MVSplat
TranSplat
HiSplat
DepthSplat
Ours
Figure Multi-resolution rendering of 3D scenes. We visualize rendered images and depth maps at three resolutions: x1 (blue box), x2 (green box), and x4 (red box). As resolution increases, artifacts in the underlying 3D representation become more evident. In the image space, they appear as dark regions caused by unfilled gaps, where hollow they appear as unnatural yellow regions, indicating incorrect depth predictions caused by geometric discontinuities or sparsity. Note that yellow corresponds near surfaces and blue denotes distant regions in depth map visualization.
rendered black pixels. In the depth space,
Datasets. To evaluate our method, we follow the experimental setup in PixelSplat Charatan et al. (2024) and conduct experiments on the RealEstate (RE10K) Zhou et al. (2018) and ACID Liu et al. (2021a) datasets. RE10K mainly consists of indoor real estate videos, whereas ACID contains outdoor scenes captured by aerial drones. Both datasets provide precomputed camera poses and adhere to the official train-test splits used in prior work. Additionally, we evaluate our method on the Jensen et al. (2014) dataset following MVSplat Chen et al. (2024b), DL.3V Ling et al.
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(2024) following DepthSplat Xu et al. (0024b). and further extend our evaluation the challenging ScanNet Dai et (2017) dataset.
Evaluation Metries. We evaluate novel view synthesis quality using standard metrics: PSNR, SSIM, and LPIPS. To better evaluate geometric fidelity, we additionally report high-resolution rendering consistency (HRRC) results at 2x and 4x resolution.
Baselines. We compare our method to state-of-the-art sparse-view generalizable methods for novel view synthesis. including PixelSplat [Charatan et al. (2024). MVSplat Chen et al. (2024b). TranSplat (Zhang et al. (2025). HiSplat Tang et al. (2024), and DepthSplat Xu et (2024b)) Among these, PixelSplat and HiSplat generate multiple Gaussians per pixel, while MVSplat, TranSplat, and DepthSplat predict a single Gaussian per pixel. Since using more primitives generally improves performance, we focus our core comparisons on the latter group to ensure a fair comparison.
Implementation Details. Our method is implemented using PyTorch Paszke et al. (2019) and optimized using Adam W [oshchilovv & Hutter (2017) with a cosine learning rate schedule. We conduct experiments with different monocular backbones from Depth Anything V2 Yang al. (2024)
(ViT-S, VIT-B, ViT-L), referred to as Ours-S, S. Ours-B, B, and Ours-L respectively. We train our models for total of 4800K iterations an NVIDIA A100 GPU following DepthSplat (Xu et al. (202b),.. For the small model (Ours-S), we train for 300K iterations with a batch size of 16, while the base and large models (Ours-B and Ours-L) are trained for 600K iterations with batch size of 8. We adopt the encoder settings from Sppth Xu et al. (2024b), but use lower learning rate of 2 x 10 for the pretrained Depth Anything V2 backbone. All other layers are trained with a learning rate of 2 x 10 The opacity threshold Topa is set to 0.6, and the alpha normalization threshold Ta is set to 0.1 during training and 0.001 during evaluation. Predicted scale multipliers are clamped to the range 33. We train our models at 256 x 256 resolution for fair comparison unless otherwise specified. Furthermore, we explore higher-resoluton training at 256 x 448 and demonstrate the results in the appendix A.3
4.1
MAIN RESULTS
Table 1: Novel view synthesis performance on the RealEistate I 10k dataset.
256 (Standard)
512x512 (HRRC)
1024
1024 (HRRC)
Average
Method
PSNR1
SSIM1
LPIPS]
PSNRT
SSIM
LPIPS]
PSNR1
SSIM
LPIPS]
PSNR1
SSIM
LPIPS]
pixelSplat
26.049
0.137
25.782
0.868
24.920
0.269
25.584
0.869
0.204
HiSplat
27.193
0.882
0.117
25.269
0.870
0.198
24.262
0.878
0.248
25.575
0.877
0,188
MVSplai
26.359
0.129
20.408
0.809
17.966
0.425
21.578
0.811
0.281
TrunSplat
26.687
0.875
0.125
20.610
0.815
0.286
18.154
0.761
0.427
21.817
0.817
0,279
DepthSplat
27.504
0.890
0.112
20.031
0.774
0.341
16.385
0.635
0.491
21.307
0.766
0,315
Ours-S
27.001
0.883
0.118
25.989
0.860
0.223
24.535
0.835
0.325
25.842
0.859
0.222
Ours-B
27.447
0.890
0.113
26.280
0,866
0.218
24.744
0.838
0.322
26.157
0.865
0.217
Ours-L
27.537
0.892
0.112
26.331
0.866
0.217
24.897
0.842
0.320
26.255
0.867
0.216
Table 2: Novel view synthesis performance on the ACID dataset.
256 (Standard)
512x512 (HRRC)
1024x 1024 (HRRC)
Average SSIM
Method
PSNR1
SSIM
I.PIPS!
PSNR1
SSIM
IPPPS
PSNR1
SSIM
I.PPS.
PSNR1
I.PIPS..
pixelSplat
28.284
0.842
0.146
27.687
0.848
0.243
26.462
0.858
0.343
27.478
0.849
0.244
HiSplat
28.737
0.853
0.132
25.376
0.833
0.246
23.988
0.841
0.314
25,700
0.842
0.231
MVSplal
28.202
0.842
0.145
0.711
0.406
14.784
0.572
0.567
20.263
0.373
TranSplat
28.337
0.845
0.143
17.911
0.716
0,402
14.956
0.582
0.358
20.401
0.714
0.373
Ours
28.336
0.845
0.144
26.868
0.814
0.281
21.253
0.690
0.457
25.486
0.783
0.294
Reconstruction Quality. We report quantitative comparison on the RE10K dataset in Table I and on the ACID dataset in Table Our proposed SuríSplat method consistently outperforms previous state- of-the-art methods across various metrics and datasets, especially under high-resolution rendering settings. As shown in Figure 4 we visualize the predicted 3D scenes rendered into both RGB and depth maps at the original, 2, and x4 resolutions. While previous methods appear visually plausible at the original resolution, their reconstructions manifest spatial inconsistencies at higher resolutions, including holes and surface gaps. These artifacts reveal the limitations of previous feedforward 3DGS
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Table 3: Cross datasets performance.
DL.DV
Average
Method
PSNRT
SSIMT
LPIPS
PSNRT
SSIM
LPIPS
PSNRT
SSIMT
LPIPSL
PSNR1
SSIMT
LPIPS
pixelSplar
19.606
0.324
27.201
0.882
0.104
12.752
0.639
19.853
0.642
HiSplar
19.095
0.691
0.342
26.242
0.869
0.112
16.019
0.671
0.277
20.452
0.744
0.244
MV Sptat
18.725
0.692
0.333
23.841
0.768
0.156
13.914
0.470
0,386
18.827
0.643
0.292
TranSplat
0.332
23.913
0.771
0.161
0.327
19.271
0.668
0,273
DopthSplar
20.201
0.735
0.305
28.141
0.905
0.083
14.592
0.425
0.436
20.978
0.688
0,275
Ours
20.305
0.731
0.313
27.384
0.890
0.106
15.544
0.488
0.329
21.078
0.703
0.249
Table 4: Ablations study various components.
256 x 256 (Standard)
512x512 (HRRC)
1024x1024 (HRRC)
Average SSIMT
Method
PSNRT
SSIM
LPIPS.
PSNRT
SSIMT
LPIPS]
PSNRT
SSIM
LPIPS)
PSNRT
LPIPS
w/o FAB, SCP
26.925
0.880
0.120
21.549
0.805
0.307
18.563
0.716
0.422
22.346
0.800
0.283
who FAB
26.481
0.873
0.128
21.042
0.776
0.345
17.576
0.662
0.474
21.700
0.770
0.316
Fall
27.001
0.883
0.118
25.989
0.860
0.223
24.535
0.835
0.325
25.842
0.859
0.222
models in capturing sub-pixcl-level geometry. Notably, DepthSplat, despite using the same encoder backbone as our method, fails to generate coherent geometry or consistent surface details, which highlights the effecctiveness of our surface continuity prior and forced alpha blending strategy. Cross-Datase Generalization. To assess cross- generalization, we train our model on RE10K and directly conduct evaluation DTU, DL3DV, and ScanNet datasets. As shown in Table B SurfSplat maintains strong performance and generalizes better than previous methods across all target domains. This demonstrates the robustness of our learned geometric prior and the general applicability of our representation even under domain shift.
4.2 ABLATION AND ANALYSIS
YZ Projection
ZX Projection
XY Projection
We conduct extensive ablation studies to further validate the effectiveness of key components. Specifically, we evaluate variants without both of forced alpha blending and surface continuity prior (denoted as w/o FAB,SCP), and without forced alpha blending (denoted as w/o FAB). Quantitative results are reported in Table and also rendered the reconstructed 3D scenes onto three orthogonal planes in Figure to pro- vide qualitative comparisons. Our full model yields continuous and coherent surfaces, while ablated variants exhibit visible artifacts and spa- tial inconsistencies.
wio
Surface Continuity Prior. To evaluate the im- pact of the surface continuity prior, we train a variant that independently predicts all Gaus- sian attributes without geometric coupling. In- terestingly, this variant still achieves competitive novel view synthesis (NVS) performance the original resolution, despite producing visually noisy and discontinuous surfaces. This observa- tion highlights a key limitation of conventional NVS metrics and underscores the value of our proposed HRRC metric, which drops significantly when surface continuity is not enforced. Forced Alpha Blending, We also train a variant with the surface continuity prior but without forced alpha blending. We observe clear spatial misalignment across views, the model tends to produce fully opaque Gaussians, which occlude background information and hinder correct 3D alignment. This leads substantial drop in both standard and HRRC metrics.
Figure 5: Ablation study: Visualization of recon- structed 3D scenes. Our full model yields contin- uous and coherent surfaces, while ablated variants exhibit visible artifacts and spatial inconsistencies.

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Input
Ours
DepthSplat
Figure 6: Normal and mesh comparison with DepthSplat.
Table 5: Quantitative performance comparison high-resolution DL3DV dataset.
Metric
pixelSplat
HiSplat
MVSplat
TransSplat
DepthSplat
Ours
PSNR SSIM f
24.082
22.780
17.966
19.545
16.066
24.411
0.755
0.765
0.645
0.679
0.600
0.788
LPIPS +
0.250
0.237
0.301
0.257
0.424
0.252
4.3 EFFECTIVENESS OF HRRC METRIC
To empirically validate the effectiveness of HRCC on native high-resolution data, we conducted additional experiments on the high-resolution version of the DL.3DV dataset. We randomly sampled representative subset for evaluation and ensured that all methods were tested under identical conditions. The results are reported Table Across these experiments, the relative performance rankings remained fully consistent with those observed under HRRC evaluation, even without any bicubic upsampling. This indicates that the conclusions drawn from HRRC reliably transfer to native high-resolution evaluations.
4.4 NORMAL AND MESH COMPARISON
Since our method naturally predicts surface orientation for each 2DGS, we additionally generate the corresponding normal maps and reconstructed meshes to further demonstrate the effectiveness of SurtSplat. We provide a comparison with DepthSplat Yang et al. (2024) in Figure From this comparison, we observe that our method produces more geometrically consistent results, highlighting the improved geometric coherence induced by the surface continuity prior.
CONCLUSION
We present SurfSplat, a feedforward framework for high-fidelity 3D scene reconstruction from sparse views using 2D Gaussian splatting primitive. By introducing a surface continuity prior and a forced alpha blending strategy, our method addresses key limitations of previous approaches, eliminating surface discontinuities and overcoming opacity collapse. We also propose the HRRC metric to better evaluate fine- anee-graine geometric fidelity. Extensive experiments across multiple datasets demonstrate that SurfSplat achieves state-of-the-ee performance across both standard and high-resolution metrics, providing scalable and accurate solution for generalizable 3D reconstruction.
Limitations. Despite these improvements, our method still relies on known camera poses, and predicting one Gaussian per pixel can lead redundant representations. These limitations open opportunities for future research joint pose elimination and compact, adaptive representations.
ACKNOWLEDGMENTE AND DISCLOSURE OF FUNDING
This work was partly supported by the NSFC (62431015, 62571317, 62501387), the Fundamental Research Funds for the Central Universities, Shanghai Key Laboratory of Digital Media Processing and Transmission under Grant 22DZ2229005, 111 project BP0719010.
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A TECHNICAL APPENDICES AND SUPPLEMENTARY MATERIAL
A.1 ENCODER ARCHITECTURE
We adopt dual-branch encoder design extract both monocular and multi-view features for robust 3D reasoning, following the architecture proposed by DepthSplat Xu et al. (004b)..
Multi-view Branch. The multi-view encoder begins with lightweight ResNet-style backbone composed of stride-2 convolutional layers, yielding spatially downsampled feature maps by factor of To enable view aggregation, we employ a multi-view Swin Transformer [iu et al. (202Ib)
consisting of 6 stacked self- and cross-attention layers. This module outputs multi-view-aware
features {Fi} {
{=1' where
N
R = x
We further adopt the plane-sweep stereo technique Collins 1996); Xu al. (2023) to construct geometric consistency. We uniformly sample D candidate depths between near and far bounds. Given
reference view and source view J. warp features Fj to view each depth dm, resulting in
These warped volumes are compared to F; via dot-product similarity to construct m=1 cost volume C E xR
Single-view Branch. We utilize the ViT backbone from Depth Anything V2 model Yang et al.
(2024) to extract monocular features. The output has a spatial resolution of 1/14 relative to the original image and is bilinearly upsampled to match the cost volume resolution, yielding monocular features Fi
RR x xCm
The monocular and multi-view features Fü and C' are concatenated along the channel dimension and processed by 2D U-Net to produce depth candidates D 4 I8 xD A softmax operation is applied over the depth axis, followed by a weighted summation to generate the predicted depth map.
U-Net and Depth Prediction.
To enhance depth quality, we employ hicrarchical cascade structure Gu et al. 2020), refining the predicted depth to D E R 2 x 2W which is subsequently upsampled to full resolution using a DPT head Ranfil al. (2021)
The predicted depth is used to reconstruct Gaussian positions. For estimating the remaining Gaussian attributes such as scale multipliers, nigh-frequecc SH coefficients, and opacity- we apply an additional DPT head, conditioned on a concatenation of the input image, predicted depth, and encoder features.
Attribute Prediction.
Hyperparameter Selection. The downsample scale is set to 4. Channel number C' is set to 128, channel number D is set to 128. The channel number Cm of the monocular feature set to 64 for small model, 96 for base model, 128 for large model.
Note: Our implementation is consistent with DepthSplat Xu al. 2024b) for reproducibility. No architectural modifications are made to the encoder unless otherwise stated.
A.2 HYPERPARAMETER SENSITIVITY.
We further investigate the influence of the hyperparameters Topa and Ta in Table Our results indicate that SurfSplat is robust to the exact threshold values, maintaining strong performance long as the thresholds remain within a reasonable range. This demonstrates the stability and generality of the forced alpha blending technique.
Table 6: Ablations study on hyperparameter sensitivity.
(Standard)
512x:12 (HRRC)
1024 (HRRC)
Average SSIMT
Method
PSNR1
SSIMI
LPIPS
PSNRT
SSIM
LPIPS
PSNRT
SSIMI
LPIPS
PSNRT
LPIPS
27.001
0.883
25.989
0.223
24.535
0.835
25.842
0.222
0.3
26.921
0.881
0.120
25.930
0.860
0.222
24.816
0.843
0.317
25.889
0.861
0.220
0.4
26.992
0.883
0.118
25.957
0,860
0.222
24.538
0.835
0.323
25.829
0,859
0.221
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A.3 EXTENDED RESULTS AT HIGHER RESOLUTION
To further demonstrate the scalability and generalization capability of our model, we train and evaluate extended version higher input resolution (256 448).
Quantitative results are summarized in Table showing consistent improvements across standard and high nigh-resolution metrics. We also visualize the rendered images and depth maps at multiple output resolutions (x1, x2, and x4) in Figure 7 Figure X detail and texture fidelity enabled by the higher-resolution input.
and Figure | highlighting the enhanced geometric
Table 7: Quantitative performance the high-resolution model.
256 456anad(aadddrrd
512X896(HRRC)
1024 (792(HRRC))
Average
Method
PSNRT
SSIMP
LPIPS
PSNRT
SSIM
LPIPS
PSNRT
SSIM
LPIPS
PSNRT
SSIMT
LPIPS]
Ours
B
26.190
0.871
0.134
25.553
0.861
0.234
24.197
0.842
0.329
25.313
0.858
0.232
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X1
Figure 7: Visualization the high-resolution model. We present rendering results (image and depth) at multiple output resolutions. As the resolution increases, model preserves coherent geometry and appearance, revealing finer details of the scene.
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Figure 8: Visualization of the high-resolution model. We present rendering results (image and depth) at multiple output resolutions. As the resolution increases, model preserves coherent geometry and appearance, revealing finer details of the scene.
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GT
X1
X2
X4
Figure 9: Visualization of the high-resolution model. We present rendering results (image and depth) at multiple output resolutions. As the resolution increases, our model preserves coherent geometry and appearance, revealing finer details of the scene.
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Reviews:
[{"review_id": "LzyaWNCfzg", "reviewer": "Reviewer_3CPe", "summary": "This paper presents a feedforward 3D reconstruction framework from sparse views based on 2D Gaussian Splatting (2DGS), as an alternative to the more common 3D Gaussian Splatting (3DGS) frameworks. Existing feedforward approaches, such as PixelSplat and MVSplat, which use 3D Gaussians as primitives, often suffer from discontinuities (holes) in the reconstructed surfaces and color-biased artifacts when given sparse-view inputs. To address these issues, this paper introduces a surface continuity regularization that enforces smoothness among Gaussians within a local 3D neighborhood. Additionally, a forced alpha blending strategy is proposed to mitigate opacity saturation during rendering. To further highlight the shortcomings of current evaluation protocols, the paper also introduces a new metric, High-Resolution Rendering Consistency (HRRC), designed to better assess geometry fidelity. Experiments on RealEstate10K, DL3DV, and ScanNet demonstrate that the proposed method achieves superior geometric and visual fidelity compared to recent feedforward baselines, especially under the HRRC evaluation.", "questions": "Please refer to weakness.\n\nMinor issues: \n* Line 193: Insufficien -> Insufficient \n\n* Line 162: the notation of camera intrinsics and poses is not clear.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* Although the overall architecture of predicting per-pixel gaussian is not new, the proposed strategy of enforcing surface smoothness and forced alpha blending is relatively new and interesting. \n\n* The motivation of the paper is clear, and the paper is well-written and the proposed method is easy to follow and the overall presentation is clear. \n\n* The paper is solving a relevant problem. Reconstructing a high fidelity surface from sparse input is a relevant problem.\n\n* Comprehensive evaluation is done on various datasets to show the effectiveness of the method.", "weaknesses": "* Why use a single Gaussian per pixel? While the method performs well against baselines that also use a single Gaussian per pixel on the novel view synthesis task (both within and cross-dataset), this design seems to limit performance compared to methods like PixelSplat and HiSplat, which use multiple Gaussians per pixel. Did the authors experiment with predicting multiple Gaussians per pixel? From Tables 1 and 2, it appears that doing so might address the observed issues more effectively than relying solely on the proposed surface continuity and forced alpha blending techniques.\n\n\n* HRRC may favor the proposed regularization strategies. While HRRC appears to be an effective metric for evaluating feedforward GS-based reconstruction frameworks, it might inherently bias the evaluation toward smoother reconstructions due to the use of upsampled ground-truth images (via bicubic interpolation). Have the authors considered training the model on low-resolution input images and then evaluating on the original ground-truth resolution? Would such an evaluation be valid or provide a fairer measure of reconstruction fidelity? A small experiment with this setting will be helpful.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Qg4OaESJ4n", "reviewer": "Reviewer_pqgf", "summary": "This paper introduces SurfSplat, a feedforward framework that replaces 3D Gaussian splatting (3DGS) with 2D Gaussian splatting (2DGS) to improve geometry reconstruction and texture fidelity in sparse-view 3D scene reconstruction. The key ideas include a surface-continuity prior linking splat shape and orientation to surface normals, and a forced-alpha blending strategy to prevent opacity collapse. The paper also proposes High-Resolution Rendering Consistency (HRRC) as a new metric to evaluate fine-grained artifacts. Experiments on RealEstate10K, DL3DV, and ScanNet show consistent improvements over baseline methods.", "questions": "1. Theoretical Motivation of the Surface-Continuity Prior\n\nCould the authors clarify the theoretical reasoning for coupling the splat’s rotation and scale with local surface orientation? Is this prior based on any assumption of local planarity or normal consistency, and how sensitive is the approach to errors in predicted normals?\n\n2. Quantitative Validation and Ablation\n    \nBeyond qualitative comparisons, could the authors provide quantitative ablations that isolate the contributions of the surface-continuity prior and forced-alpha blending? For example, what are the PSNR / SSIM / HRRC changes when each component is removed?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Solid methodological design:\nUsing 2D splats (surfels) instead of 3D Gaussians is an interesting design choice that simplifies the rendering pipeline while retaining expressive power. The authors carefully justify why this helps preserve anisotropy and continuity.\n2. Comprehensive experiments:\nResults on multiple datasets (RealEstate10K, DL3DV, ScanNet) demonstrate robustness and consistent improvements across settings. Qualitative visualizations effectively support the claims.", "weaknesses": "1. Limited theoretical grounding for the continuity prior:\nWhile the prior is intuitive, its mathematical justification remains somewhat heuristic. The authors could discuss more explicitly how the coupling of rotation and scale influences convergence and stability.\n2. Ablation analysis could be deeper:\nThe ablations are primarily qualitative. Quantitative ablations showing how much each component (surface prior, alpha blending, HRRC) contributes to the final performance would strengthen the empirical evidence.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "fOC0wjb4sY", "reviewer": "Reviewer_VQdd", "summary": "This paper targets the common failure mode of feedforward 3D reconstruction from sparse-view inputs, where existing approaches (e.g., 3DGS-based pipelines) tend to produce discontinuous, fragmented surfaces that exhibit conspicuous artifacts under close-up inspection.\n\nCore contributions:\n1、SurfSplat framework. The authors advocate using 2D Gaussian primitives (2DGS) as surface-oriented rendering elements better suited to representing continuous manifolds.\n2、Surface Continuity Prior (SCP). The key innovation. Instead of letting the network blindly regress each Gaussian’s rotation and scale, SCP explicitly derives orientation and anisotropic extent from local surface normals computed on neighborhood 3D points. This imposes a geometric prior that enforces local surface continuity and smoothness of the reconstructed geometry.\n3、Forced Alpha Blending (FAB). A training strategy that caps per-primitive opacity, preventing foreground splats from saturating alpha and completely occluding background layers. By ensuring gradient flow to deeper/occluded structures, FAB mitigates training instabilities and geometric misalignment.\n4、HRRC metric. A new evaluation metric—High-Resolution Rendering Consistency (HRRC)—that reveals holes, cracks, and other geometric defects which standard image-space metrics often miss, by evaluating renderings at higher magnification.\n\nSummary. By combining a strong geometric prior (SCP) with a stabilization strategy for compositing (FAB), the paper substantially improves the geometric fidelity and surface continuity of feedforward reconstruction models, and demonstrates the resulting advantages using the proposed HRRC metric.", "questions": "1、Potential Bias in the Proposed HRRC Metric: The HRRC metric compares the high-resolution rendered output against a ground truth image upsampled via bicubic interpolation. Since bicubic interpolation is inherently a smoothing filter that blurs fine textures and sharp edges, could this introduce a bias? Specifically, might this metric favor models that produce smoother, slightly blurred results (closer to the interpolated GT) over models that generate sharp but slightly imperfect details (which might be closer to the true high-frequency information)?\n\n2、Weak Theoretical Support for the Forced Alpha Blending (FAB) Strategy: While FAB is shown to be effective, it essentially functions as a direct \"engineering trick.\" The strategy of hard-clipping opacity and normalizing the final color lacks sufficient theoretical justification. This approach may introduce new rendering biases, particularly when handling semi-transparent objects or complex lighting, raising concerns about its robustness.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1、High Coherence between Representation Choice and Prior (2DGS + SCP): The paper appropriately pivots to 2DGS as fundamental \"surfel\" elements to address the degradation issues of 3DGS under sparse views. Its core contribution lies in using the Surface Continuity Prior (SCP) to explicitly derive the rotation and scale of each primitive from local geometry, shifting the paradigm from \"blind learning\" to \"geometrically-constrained learning.\" This effectively enhances the continuity and realism of the generated surfaces.\n\n2、Proposal of a Critical and Practical Evaluation Metric (HRRC): The paper contributes the HRRC metric, which simulates a \"close-up inspection\" by rendering at higher resolutions. This method effectively exposes hidden geometric artifacts (e.g., holes and discontinuities) that standard metrics fail to capture. This provides strong support for the paper's claims and offers a valuable new tool for the community to assess the quality of sparse-view reconstruction.\n\n3、Pragmatic Integration of a Dual-Branch Encoder (Monocular Prior + Multi-View Geometry): The architecture is pragmatically designed, leveraging the strong monocular prior from Depth Anything V2 and the multi-view consistency from plane-sweep cost volumes. This clear division of responsibility—monocular for completing missing textures and multi-view for ensuring consistency—places the model on a level playing field with current SOTA methods (e.g., MVSplat/DepthSplat series), ensuring a fair and effective comparison.\n\n4、Clarity in Writing and Structure: The paper is well-structured and clearly written, with a logical flow that is easy to follow. The authors articulate the problem, the proposed solution, and its motivation, allowing readers to quickly grasp the core contributions and technical details, which is a significant merit for a high-impact publication.", "weaknesses": "1、Innovation Granularity Focused on Regularization and Priors; Lacks Theoretical Depth: The architecture (2DGS, dual-branch encoder) largely follows recent work. The core novelty is concentrated in the SCP+FAB+HRRC components. While the engineering results are impressive, the theoretical analysis of the SCP prior remains empirical (e.g., regarding its boundaries for curvature, texture frequency, or noise). Furthermore, a systematic comparison against alternative designs, such as \"direct regression + regularization\" (e.g., curvature smoothing, normal consistency loss), is notably absent.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "HcCy6mmU1q", "reviewer": "Reviewer_6ADx", "summary": "SurfSplat is a novel method for feed-forward scene reconstruction from sparse posed-input images. Unlike previous works that employ 3D Gaussian Splats (GS) reconstructions, SurfSplat proposes a 2DGS approach. This method generates a 2DGS per pixel and incorporates two targeted additions to achieve this. The first one aligns the normals based on the local neighborhood in screen space, ensuring Surface Continuity. The second employs an enforced alpha blending technique that enhances the contribution of occluded Gaussians in the final result. This combination leads to a cleaner reconstruction where the occluded Gaussians remain optimized and contribute to the overall reconstruction quality.\nFor the reconstruction process, the authors extract features using two ViTs: one for single views and the other for aggregated multi-view information. These concatenated features are then transformed into 2DGS parameters using a 2D U-Net. Additionally, the authors propose a novel metric called HRRC, which renders the scene in higher resolution and compares it with upscaled ground truth images. This comparison highlights any inconsistencies or holes in the reconstructed surfaces.", "questions": "- Given that 2DGS are frequently easier to mesh, how do the scenes appear after meshing?\n- Does the surface continuity prior use a technique to disable or weaken it for discontinuities? How are the 2DGS behaving in this areas?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The additions to extend feed-forward-based reconstruction methods to 2DGS are well-motivated and explained.\n- Overall, the paper is easy to read and follow.\n- Utilizing screen-space information to construct a local tangent frame for continuous alignment of the 2DGS based on its neighborhood is a sound strategy. The implementation is also well explained.\n- The forced alpha blending is particularly interesting in the feed-forward reconstruction scenario, where each pixel generates a GS that overparametrizes the scene.\n- The proposed HRRC metric is intuitively comprehensible and effectively identifies critical issues like holes in the reconstruction.", "weaknesses": "- Although the HRRC metric is intuitively comprehensible, it would be beneficial to also demonstrate the surface normals from the 2DGS. This would not only highlight the smoothness but also serve as an indicator of the Surface Continuity Prior’s effectiveness.\n- Furthermore, it would be beneficial to run the method on synthetic scenes and report F1 scores or normal alignment metrics. These metrics would provide insights into the accuracy of the geometry prediction.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)