UrbanGS: Efficient and Scalable Architecture for Geometrically Accurate Large-Scene Reconstruction
Authors: Changbai Li
Haodong Zhu
Hanlin Chen
Xiuping Liang
Tongfei Chen
Shuwei Shao
Linlin Yang
Huobin Tan
Baochang Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: 3DGS; 3D surface reconstruction
Primary area: learning on graphs and other geometries & topologies

Abstract: While 3D Gaussian Splatting (3DGS) delivers high-quality, real-time rendering for bounded scenes, its extension to large-scale urban environments introduces critical challenges in geometric consistency, memory efficiency, and computational scalability. We present UrbanGS, a scalable reconstruction framework that effectively addresses these challenges for city-scale applications.
We propose a Depth-Consistent D-Normal Regularization module. In contrast to existing approaches that rely solely on monocular normal estimators—which effectively update rotation parameters but poorly optimize other geometric attributes—our method integrates D-Normal constraints with external depth supervision. This enables comprehensive updates of all geometric parameters. By further incorporating an adaptive confidence weighting mechanism based on gradient consistency and inverse depth deviation, our approach significantly enhances multi-view depth alignment and geometric coherence.
To improve scalability, we introduce a Spatially Adaptive Gaussian Pruning (SAGP) strategy, which dynamically adjusts Gaussian density based on local geometric complexity and visibility to reduce redundancy. Additionally, a unified partitioning and view assignment scheme is designed to eliminate boundary artifacts and optimize computational load. Extensive experiments on multiple urban datasets demonstrate that UrbanGS achieves superior performance in rendering quality, geometric accuracy, and memory efficiency, offering a systematic solution for high-fidelity large-scale scene reconstruction.

Published as a conference paper at ICLR 2026
URBANGS: A SCALABLE AND EFFICIENT ARCHI- TECTURE FOR GEOMETRICALLY ACCURATE LARGE- SCENE RECONSTRUCTION
Changbai Li Haodong Zhu?; Hanlin Chen', Xiuping Liang", Tongfei Chen² Shuwei Shao", Linlin Yang" Huobin Tan
Baochang Zhang
'Hangzhou International Innovation Institute, Beihang University
PInstitute of Artificial Intelligence, Beihang University
School of Software, Beihang University
*State Key Laboratory of Media Convergence and Communication, Communication University of China
*Department of Computer Science, National University of Singapore
Connroll Science and Engineering. Shandong University
7 Artificial Intelligence Research Center, Lobachevsky State University
Medill stau (MB Jog sealn
Memory
Figure 1: We propose UrbanGS, a scalable framework for high-fidelin fidelity large-scale scene reconstruc- tion. Left: It reconstructs complex urban environments from nult RGB images, capturing fine details like trees, buildings, and roads. Middle: Compared with CityGS-v2 (Liu et al. (024b) and VCR-Gaus (Chen et al.] 2024b), by comparing rendered depth maps, method intuitively demonstrate its geometric advantages terms of the surface smoothness of objects. Top-right: Our Spatially Adaptive Gaussian Pruning enables significant model compression while preserving quality. Bottom-right: UrbanGS efficiently reconstructs large scenes on A5000 GPUs, whereas
VCR-Gaus (Chen al. 2024b) fails due to out-of of-memory issues.
ABSTRACT
While 3D Gaussian Splatting (3DGS) enables high-quality, real-time rendering for bounded scenes, its extension to large-scald urban environments gives rise to critical challenges in terms of geometric consistency, memory efficiency, and com- putational scalability. To address these issues, we present UrbanGS, scalable re- construction framework that effectively tackles these challenges for city-scale ap- plications. First, we propose a Sopth-Connssttet D-Normal Regularization mod- ule. Unlike existing approaches that rely solely on monocular normal estimators. which can effectively update rotation parameters yet struggle to update position parameters, our method integrates D-Normal constraints with external depth su- pervision. This allows for comprehensive updates of all geometric parameters. By further incorporating an adaptive confidence weighting mechanism based on
"Equal contribution.
Corresponding author: 1yang@cuc.edu.co thbin Touaa en

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gradient consistency and inverse depth deviation, our approach significantly en- hances multi-view depth alignment and geometric coherence, which effectively resolves the issue of geometric accuracy in complex large-scale scenes. To im- prove scalability, we introduce a Spatially Adaptive Gaussian Pruning (SAGP) strategy, which dynamically adjusts Gaussian density based on local geometric complexity and visibility to reduce redundancy. Additionally, a unified partition- ing and view assignment scheme is designed eliminate boundary artifacts and optimize computational load. Extensive experiments on multiple urban datasets demonstrate that UrbanGS achieves superior performance in rendering quality, geometric accuracy, and memory efficiency, providing a systematic solution for high-fidelity large-scale scene reconstruction.
INTRODUCTION
3D scene reconstruction is long-standing research topic in computer vision and computer graphies, with its core objective of achieving photorealistic rendering and accurate geometric reconstruction. Following the introduction of Neural Radiance Fields (NeRF) (Mildenhall al. 202TD. 3D Gaus- sian Splatting (3DGS) (Kerbl et al., 2023) has emerged as a mainstream technique in this field, thanks to its advantages training convergence and rendering efficiency. 3DGS represents scenes using a set of discrete Gaussian ellipsoids and leverages highly optimized rasterizer for render- ing. However, due to the unstructured nature of 3DGS, accurately representing serfaaess-sseccine in large-scale complex scenes-remains significant challenge. In recent years, numerous promi- nent studies (Huang et al. [0244a Chen et al., 2024b a) have been proposed to address this issue. While these methods have achieved remarkable success in single-object or small-scale scene recon- struction, directly extending them to complex large-scale scenes reveals several critical limitations. For instance, vanilla 3DGS suffers from inadequate geometric modeling accuracy and incomplete parameter updates when applied to city-scale environments, failing to meet the high-fidelity recon- struction requirements of complex urban scenes.
To tackle the challenges of urban-scale modeling, various technical solutions have been developed. Methods such CityGaussian (Liu et al. 2024a) and VastGaussian (Lin et al., 2024) have proposed block-wise partitioning strategies; although these strategies improve rendering efficiency, they still suffer from geometric inconsistencies, low geometric accuracy, and fail to reduce memory require- ments during training. CityGaussianv2 (Liu et al. (024b) adopts a hybrid approach integrating 2D Gaussian Splatting (Huang et al. 2024a), while this accelerates training and enhances geometric accuracy, it comes at the cost of degraded rendering quality. Furthermore, vanilla 3DGS generates excessive redundant Gaussian primitives in homogeneous regions (e.g., skies, distant building fa- cades), and naive pruning heuristics often sacrifice fine-grained details (Fan et al.) 2023). Existing partitioning schemes also introduce computational inefficiencies by processing irrelevant views and generating boundary discontinuities (Liu et al.) 2024a). These limitations underscore the urgent need for a unified framework that balances geometrie precision, memory efficiency, and seamless scalability.
We propose UrbanGS, a strategy that achieves high geometric accuracy, fidelity, and efficiency in large-scale scene reconstruction. To enhance geometric fidelity in large-scale settings, we directly supervise the rendered normal maps of 3D Gaussians with external pseudo-normal priors. However, this form of supervision alone is insufficient for updating the position parameters of Gaussians, which is critical for accurate surface reconstruction (Chen et al., 2024b). To overcome this limita- tion. we introduce Depth-Consistern D-Normal Regularization framework. Instead of supervising the rendered normals directly. we first derive depth-normal (D-Normal) from the spatial gradient of the rendered depth maps, which are then supervised by the pseudo-normal priors. This estab- lishes geometric constraint intrinsically linked to depth, thereby enabling comprehensive updates of both rotation and position parameters of the Gaussians. Furthermore, considering the limitation that supervision based on D-Normal relies the accuracy of rendered depth maps, we introduce a depth estimator (Pseudo Depth) (Hu et al. 2024) to directly supervise the rendered depth maps, thereby constructing the "Pseudo Depth & D-Normal Dual Supervision Mechanism" (with theoret- ical proofs provided in the supplementary materials). To ensure the reliability of depth alignment across multiple views, we propose adaptive confidence weighting strategy that dynamically ad-
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justs supervision weights for different regions, thus reducing the impact of depth enrors surface reconstruction results.
To meet the memory and computational demands of urban-scale reconstruction, propose a Spa- tially Adaptive Gaussian Pruning (SAGP) method. Traditional pruning approaches, designed for small-scale or object-level scenes, rely on global metrics fixed thresholds (Fan et al. 2023 When applied to city-scale scenes with high spatial heterogeneity and numerous Gaussian primitives, such strategies often oversimplify local structures or lose fine details (see Table 4. To our knowledge. this is the first pruning framework specifically designed for city-scale 3D Gaussian Splatting. SAGP operates within local voxel cells, integrating local geometric complexity. ray- may-intersection frequency, and visibility importance scores to decide which Gaussians to prune. This adaptively removes redundant primitives Suimitiyss-especiallll in uniform or distant regions-w while preserving perceptually and geometrically critical structures. Applied progressively during training. SAGP significantly reduces model complexity and memory usage while maintaining high rendering and geometric quality (Ta- ble Fig. 4.. We also incorporate a partitioning strategy (Liu et al., (022a)) to enable parallel processing, supporting efficient and scalable reconstruction of large-scale urban scenes.
Our main contributions are summarized below:
We propose Depth-Consistent D-Normal Regularizer that enables holistic optimization of all Gaussian parameters (position, rotation), addressing the limitation of incomplete geometric up- dates in methods that supervise only rendered normals.
We introduce an adaptive confidence term to enhance robustness, which suppresses unreliable depth predictions and strengthens multi-view geometric alignment.
To address Gaussian redundancy and memory explosion city-scale scenes, we design a Spa- tially Adaptive Gaussian Pruning (SAGP) algorithm that is aware of local geometric complexity. Extensive experiments demonstrate that method outperforms existing large-scale scene construction techniques, thus laying a solid foundation for future further research in this field.
RELATED WORK
Neural Rendering. Novel view synthesis and multi -iew surface reconstruction are interconnected tasks in 3D scene reconstruction. Traditional reconstruction pipelines relied on Structure-frot
Motion (SfM) (Duisterhof et al. 2024 Wang al. 2023 He et al. 2024) and Multi- Stereo (MVS) Furukaaw et al.) 2015 Tang el al. 2024b Yao et al. 2018) with feature matching (Wang &
Shen 2018). but suffered from artifacts and noise sensitivity (Leroy al. 2024). Early synthesis
methods like Soft3D (Penner & Zhang 2017) used volumetric ray ray-marching with high computa-
tional costs. The neural revolution began with NeRF (Mildenhall et al. (02T), which improved qual-
ity through positional encoding yet remained slow due to MLPs; variants like Mip-NeRF (Barron
jet al. 2022). InstantNGP (Müller et al.) 2022). and Plenoxels (Fridovich- KeeiovicccKKilll et al. 2022) balanced
efficiency but struggled with empty spaces, For reconstruction. implicit methods like NeuS (Wang
jet al., 202T) and Neuralangelo (Li et al., (023) integrated SDFs (Park et al., 2019 Yu et al. 2022)
for detailed surfaces at the cost of lengthy training. The paradigm shifted with 3D Gaussian Splat- ting (3DGS) (Kerbl et al.) 2023), enabling real-time synthesis via unstructured Gaussians, though its explicit form caused reconstruction issues like depth ambiguities (Zhang et al. 2024 Chen al.) 2024a). Subsequent optimizations addressed both domains: synthesis-focused improvements in- cluded Mip-splatting (Yu et al.) (2224)), while reconstruction enhancements featured SuGaR's mesh binding (Guédon & Lepetit, (024) (despite scalability limits (Chen et al. (024a)). 2DGS's surfel- based normal alignment (Huang al. 2024b), VCR-GauS's depth-normal regularizers (Chen al. 2024b), and GOF's 's ray- cay-tracing for unbounded scenes (Yu et al., (0244)). However, when reconstruct- ing complex large-scale scenes, 3DGS faces considerable challenges in terms of rendering quality and geometric accuracy. Furthermore, it also has the problems of a surge in video memory usage and excessively long training times, all of which limit the further expansion of 3DGS in large-scale scenes.
Large-Scale Scene Reconstruction. Reconstructing large-scale scenes (e.g., urban areas, expansive landscapes) faces significant challenges, including computational inefficiency, memory constraints, and geometric inconsistencies across sub-scenes processed in a block-wise manner (Tancik et al,
2022
Early NeRF-based methods partitioned scenes into blocks for parallel training (Turki et al.
2022 Zhang et al.) 2025), but due to the limitations of multi-layer perceptrons (MLPs), these ap-
proaches suffered from slow rendering speeds and poor scalability (Kerbl et al. 2023). Although
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3DGS-based methods improved efficiency, they introduced new issues: partition-and and- merge strate- gies such as VastGaussian (Lin et al. 2024) often lead to boundary inconsistencies due to insuffi- cient multi-view constraints; methods like CityGaussian (Liu et al. 2024a) require time-consumiing post-processing for pruning or distillation; and while these methods improve rendering quality, they still struggle with geometric accuracy, training cost, and efficiency (Chen & Lee 2024). More cently, CityGS-X (Gao et al. 2025) revisits large-scale 3DGS from a systems perspective, introduc- ing parallel hierarchical representation with multi-task supervision and progressive optimization that eliminates the partition-ande -ad-o---nddeedee- pipeline and improves geometric consistency under scalable multi-GPU training. but its surface reconstruction quality for figh-fidelity urban details remains limited. Opiimization-focuset solutions like CityGaussian (Liu et al., 2024b), despite employing techniques to control Gaussian proliferation, sacrifice rendering quality to some extent. To address these limitations, we propose the UrbanGS framework, which establishes unified depth-norme regularizer for holistic geometric optimization, incorporates confidence-awie robustness, introduces spatially adaptive pruning manage redundancy, and designs scamless par- titioning scheme, collectively achieving high-fidelity, efficient, and geometrically consistent large- scale reconstruction.
weighting to enhance
METHODOL.OGY
3.1 PRELIMINARIES
3D Gaussian Splatting, 3D Gaussian Splatting models a scene using a collection of anisotropic 3D Gaussians G = (G; NJ. Each 3D Gaussian unit G; is characterized by center u E R and covariance matrix E 3x3 and can be mathematically expressed as:
1 G.(p) = exp - (p w,) M, (p uz)
(I)
During the training process, the covariance matrix is decomposed into a rotation matrix It E 3g3 and diagonal scaling matrix S E 3x33 that is,
Σ RSS R
(2)
to ensure the covariance matrix is positive semi-definite, For rendering the color of a pixel p, the 3D Gaussians are projected into the image space for alpha blending:
C
Σ c:a9 (1-a))
(3)
where e; and a; oz:()) denote the color and density of a point, respectively.
3.2 DEPTH-CONSISTENT D-NORMAL REGULARIZATION
D-Normal Regularization, To reconstruct scene surfaces, we enforce normal priors N predicted by pretrained monocular deep neural network (Bac & Davison 2024) to supervise the rendered normal map N using L1 and cosine losses:
L, || N111 + (1 Ñ N).
(4)
In our method, the depth map is rendered by performing a weighted sum of depths (Bae & Davison 2024 Chen et al. 2024h) Yu et al. 2022), with the formula given as follows:
ΣIEM d,a; IT - -a))
D
(S)
ΣIEM 0; 1T a.))
where d, denotes the intersection depth (Chen al. 2023 2024b) and is distinct from the depth estimation in conventional 3D Gaussian Splatting (3DGS). Specifically, refers to the distance from the camera the intersection point calculated along the 7-axis of the camera coordinate system; this
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Merge Fasion
Depth Coniisiat D.Normal
Reqularization
Assignment
Spatially
Spothaly Adapthe Gamslan
Presligg
Proniog
Paralkd Block Refieement
(a)Training Pipeline
Regularization
Computatin
Gausiaus
Rendured Normal
Reudered Depth
Normatraaton
Camsing Prraaing
Ronder
Depth
Sat Uiazer Transters
Global Caussians
Voxel Grld
Depth-Consistent D-Normal Regularization
Spatially Adaptive Gaussian Pruning
UrbanGS training pipeline and core components. (a) Training Pipeline: Starting from coarse global Gaussians, apply spatially adaptive Gaussian pruning obtain compact priors, contract and partition the scene into blocks, assign camera views using geometric and SSIM-based criteria, and refine all blocks in parallel before merging them into unified large-scale 3D Gaussian scene. (b) Depth-Consistent D-Normal Regularization: 3D Gaussians are rendered to depth and normal maps. depth is converted D-normals and jointly supervised with pseudo-depth and pseudo- normal priors from pretrained models via the loss Cn Lan + wgLid. yielding stable and globally consistent geometry. (c) Spatially Adaptive Gaussian Pruning: Global Gaussians are discretized into a voxel grid, where per-cell importance We,s and view dependentt cues are fused into pruning scores to remove redundant Gaussians and obtain an efficient yet accurate representation.
Figure 2:
intersection point is formed between the ray emitted from the camera center and the elliptical plane obtained by compressing the ellipsoid of 3DGS (Further details are provided in the supplementary material in the Appendix).
Additionally, effectively update Gaussian positions, we utilize the predicted normal N from the pretrained model to supervise the D-Normal Na. The derivation of the D-Normal from the rendered depth involves two sequential steps. First, the rendered depth map is back-projected into point clouds d }, using the camera intrinsic matrix. Subsequently, the horizontal and vertical finite differences are computed between adjacent points this back projeproeet point cloud; the D-Normal is then obtained by calculating the tross-product of these two sets of finite differences.
V p) x Vnd(n,p)
Na(n. p)
(6)
Ved x V
where d represents the 3D coordinates of pixel obtained via back-projection from the depth map. We then apply the D-Normal regularization:
Can Na NI1, + (a-Na-
Depth Consistency Regularization. In urban-scale scenes, D-Normal regularization optimizes ge- ometry through normal-depth associations but lacks explicit cross-view depth constraints, frequently causing building misalignment and street distortion-especiin inconsistent multi-view depth predictions, we propose a depth consistency framework integrating in- confidence. This extends normal-base regularization
in distant/complex areas. To resolve
verse depth constraints with geometry- by incorporating robust priors from monocular depth estimators, where depth anchors De., (Hu et al, 2024) ensure cross-view consistency during optimization.
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pre-trained Specifically, we derive dense relative depth anchors by processing training images with a pre- DepthAnything-Vv v2 model (Hu et al., 2024). To align these monocular predictions with the unified metric scale of the 3D reconstruction, from-Motion (SfM) (Schönberger & Frahm, 2016). Specifically, we compute per-view scale and shift parameters by robustly fitting the monocular depth maps to the sparse COLMAP depth values at valid 2D-3D correspondences. This process brings the relative depth estimates into alignment with the scale of the multi-view geometry. We define inverse depth loss Lid that operates on reciprocal depths to balance optimization sensitivity across distance ranges (Kerbl et al. 2024):
leverage sparse 3D points from COLMAP's Structure-
Cia u, v) = | 11D-~(uu u) Dixl (u, v)
(
where 1i-1= 1/D the reciprocal of the rendered depth map. This formulation minimizes rel- ative depth errors per pixel while enhancing distant surface accuracy where linear depth gradients diminish. Complementing this loss, we define a geometry- cewae confidence measure we based on two geometric cues. First, the cosine similarity of depth gradients:
VD. V
cOs Φ
I 1VD12 Dexxll22
quantifies gradient reliability by measuring local surface orientation consistency. Second, we mea- sure error sensitivity via normalized inverse depth deviation suppress high-discrepancy regions:
Lia(u, v) median (D
Ca(u, v)
(10)
The unified confidence wa combines both cues through exponential decay:
cos Φ -
Ed
Wl exp
0.01
exp
0.1
(11)
where hyperparameters " 0.01 and 0.1 balance directional and magnitude sensitivity. The total optimization objective is consequently augmented to:
Lixtal LRGB A1 + Azhan + As fua).
(12)
where Xi 3) balancing the individual components. LRGB includes Li and D-SSIM
losses (Kerbl et al., 2023
3.3 SPATIALLY ADAPTIVE GAUSSIAN PRUNING (SAGP)
Large-scale 3D scenes exhibit strong spatial heterogeneity: detailed foreground regions require dense Gaussians to capture fine structures, whereas distant areas often suffer from excessive Gaus- sian proliferation, leading to high memory cost and degraded rendering. Existing pruning strategies based global metrics fixed opacity thresholds (Kerbl et al.) 2023 Fan et al.) (003) tend to oversimplify local details or remove important far- fielf Gaussians, resulting in incomplete recon- structions and visual artifacts.
To overcome these limitations, we propose a unified, spatially adaptive pruning framework. The scene is first partitioned into volumetric cells whose characteristic length & scales with the overall Gaussian density:
Vscene N
1/3
(13)
where Vacone denotes the bounding-box volume and W the total number of Gaussians. We set 1.2 to slightly enlarge the cell size for more stable local statistics.
Within each cell, we compute the f-th percentile Gaussian volume ( and normalize individual volumes via sub-linear transform:
1)
Wali
min
(14)
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We use t 90% to represent the typical volume in each cell while mitigating outlier influence. The sub-linear exponent ń = 0.5 (i.c., a square root) is applied to compress the dynamic range of volume ratios, thereby amplifying the importance of fine-scale structures while suppressing overly large Gaussians. This operation attenuates oversized background Gaussians while amplifying fine- scale structures, thereby establishing context-aware basis for importance estimation. Building on these localized volume weights, we define the importance score S; for each Gaussian as the prod- of three normalized geometric and photometric attributes: the normalized ray-intersection fre- quency O; max ry? where counts the intersections between the i-th Gaussian and sampled
"i
rays during training; the Sigmoid-mapped opacity т; a(a)) 1
14e-", derived from the learnable opacity parameter aş; and the sub-linear volume weight We,s from Eq. given by
14
The combined score is
S o: Wejs;
(15)
which eliminates the need for manually tuned weighting hyperparameters- detailed discussion of linear combinations provided in the supplementary material(Sec,, (D.) and ensures that a Gaussian is retained only when it simultaneously exhibits high visibility, frequent observation across views, and appropriate geometric scale.
3.4 PARTITIONING STRATEGY
Our partitioning strategy is improved based CityGS Liu al. 2024a), illustrated in part (a) of Fig. 2 First, when obtaining the global coarse 3DGS model, we first eliminate redundant Gaus- sians through SAGP pruning prevent these redundant Gaussians from attracting non-contributing views and amplifying the computational load during subsequent block-wise training. Then, in the partitioning phase, retain common Gaussian primitives at the boundaries of each sub-block to avoid introducing visible fusion artifacts caused by geometric discontinuities between blocks. All other modules follow the methodologies of CityGS, and the specific formulas are referred to in the supplementary materials C
4 EXPERIMENTS
Table 1: Quantitative comparisons on the Mill19 Yu et al. 2022) and UrbanScene3D (Ln et al. 2022) datasets for novel view synthesis. 1 indicates higher better, while |, indicates lower better. The top three results are highlighted with red, orange, and yellow backgrounds, respectively. denotes results obtained without the decoupled appearance encoding.
Building
Rubble
Residence
Sci-Ar
SSIM f PSNR LPIPS
SSIM
PSNR
LPIPS &
SSIM PSNR 1 L.IIS 1 SSIM
PSNR 1 LPIPS
w/o Geometric Optimization
Mega-NeRF
0.547
20.92
0.454
0.553
24.06
0.508
0.628
22.08
0.401
0.770
25.60
0.312
Switch Switch-NeRF NeRF
0.579
21.54
0.397
0.562
24.31
0.478
0.654
22.57
0.352
0.795
26.51
0.271
VastGaussian
0.728
21.80
0.225
0.742
25.20
0.264
0.261
0.761
22.64
0.261
3DGS
0.738
22.53
0.214
0.725
25.51
0.316
0.745
22.36
0.247
0.791
24.13
0,262
DoGaussian
0.759
22.73
0.204
0.765
25.78
0.257
0.740
21.94
0.244
0.804
24.42
0.219
CayGaussian
0.778
21.55
0.246
0.813
25.77
0.228
0.813
22.00
0.211
0.837
21.39
0.230
w/ Geometric Optimization
SuGaR
0.507
17.76
0.455
0.577
20.69
0.453
0.603
18.74
0.406
0.698
18.60
0,349
NeuS
0.463
18.01
0.611
0.480
20.46
0.618
0.503
17.85
0.533
0.633
18.62
0.472
Neuralangelo
0.582
17.89
0.322
0.625
20.18
0.314
0.644
18.03
0.263
0.769
19.10
0.231
PGSR
0.480
16.12
0.573
0.728
23.09
0.334
0.746
20.57
0.289
0.799
19.72
0.275
VCR-
0.502
19.56
0.502
0,541
21.34
0.428
0.623
20.59
0.359
0.665
19.31
0.465
Cityaassin
0.650
19.07
0.397
0.720
23.75
0.322
0.769
21.15
0.234
0.810
20.66
0,266
Ours
0.802
22.82
0.208
0.791
26.25
0.210
0.823
22.48
0.205
0.824
22.62
0.279
4.1 EXPERIMENTAL SETUP
Our experiments cover seven representative scenes drawn from four datasets: Building and Rubble from Mill-19 Yu et al. 2022): Residence and Sci-Art from UrbanScene3D (Lin et al.) 2022): 2) and Residence, Russian Building, and Modern Building from GauU-Scene (Xiong et al. 2024). Un- less otherwise noted, competing methods were evaluated RTXA800 GPUs. while UrbanGS was trained on eight RTXA5000 GPUs. Additional details on training protocols and evaluation settings provided in the supplementary material.
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Ground Truth
GOF
VCR-Gaus
CityGS-v2
Ours
Figure 3: Qualitative results of ours and other methods image rendering on Mill-19 Yu et al, 2022) and Urbanscene3D (Lin et al. 2022).
Ground Truth
Ours
ChyGS-X
Cit
2DGS
VCR-GauS
Campus
College
Morden
Village
Figure 4: Qualitative mesh and texture comparison between SOTA and method GauU-Scene dataset (Xiong et al. 2024).
4.2 MAIN RESULTS
Novel View Synthesis. As shown in Table I and Fig. we present quantitative and qualitative evaluations large-scale scene reconstruction methods with and without geometric optimization. UrbanGS consistently achieves state-of-the-art performance, attaining the highest PSNR and SSIM in building scenes and reducing LPIPS by 0.006 over CityGS (Liu et al. (2024a) in residential scenes. Qualitative results in Fig. further show reduced floating artifacts, indicating stronger multi-view consistency and more faithful appearance preservation for large-scale reconstruction.
Surface Reconstruction. We compare method with existing surface reconstruction approaches on the GauU-Scene datasets (Xiong et al. 2024). As shown in Table our method achieves state-of- the-art performance among both neural implicit baselines and recent IDGS-based city-scale meth- ods. In particular, compared with CityGS-X. X, our approach attains higher Fl scores across all scenes by improving recall while maintaining comparable precision. It also surpasses CityGS-v2 (Liu et al.
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Table 2: Detailed geometry evaluation on the GauU-Scene dataset (Xiong al. 2024 "NaN" indicates that the method produced invalid numerical results, while "FAIL" denotes failure to extract valid mesh. For all metrics, f indicates that higher values are better.
Residence
Russian Building
Modern Building
Methods
Pf
F1
Pt
f
f
Pf
1
F1 f
NeuS
FAIL
FAIL
FAIL
FAIL
FAIL
FAIL
FAIL
FAIL
FAIL
Neuralangelo
NaN
NaN
NaN
FAIL
FAIL
FAIL
NaN
NaN
NaN
SuGaR
0.579
0.287
0.384
0.480
0.369
0.417
0.650
0.220
0.329
GOF
0.404
0.418
0.411
0.294
0.394
0.330
0.411
0.357
0.382
VCR-Gaus
0.498
0.402
0.445
0.538
0.454
0.492
0.591
0.401
0.478
2DGS
0.526
0.406
0.458
0.544
0.519
0.531
0.588
0.413
0.485
CityGS-X
0,512
0.411
0.456
0.572
0,516
0.542
0.653
0.389
0,487
Ciyyaausian
0.524
0.421
0.467
0.560
0.530
0.544
0.643
0.398
0.492
Ours
0.529
0.461
0.493
0.568
0.525
0.546
0.662
0.408
0.503
Table Under the GauU-Scene dataset (Lin al. 2022), cling Methods, the best result for specific metrics under each scene is highlighted in bold.
comparison of Large-Scale Scene Mod-
Scene
Method
PSNR f
F1 f
#GS(M)
Size(G) | Mem.(G). 4
CityGS
23.17
0.453
8.05
0.44
31.5
Residence
C2tyGS-v2
23.46
0.465
8.07
0.44
14.2
Ours
23.78
0.493
7.78
0.37
13.2
CityGS
24.19
0.455
7.00
0.38
27.4
Russia
CityGS-v2 v2
23.89
0,537
6.97
0.38
15.0
Ours
24.53
0.546
6.56
0.35
11.4
CityGS
26.22
0,462
7.90
0.43
29.2
Modern
CityGS-v2
25.53
0.489
7.90
0.42
16.1
Ours
26.44
0.503
7.45
0.39
15.0
Efficiency Comparison
Quality Comparison
CityGS-v2
145m
Cityo
26.15
DoGaussian
VastGS
185m
3DGS
190m
25.20
SudichNNoR
Meg RF
2m
Megs-NeRF
24,00
250
1500 Training (min)
500
750
1000
1250
1750
2000
23.5
24.0
24.5
25.0
25.5
26.0
26.5
27.0
(dB) Higher
Figure 5: Experimental results on the Rubble dataset Yu et al. 2022) demonstrate that the proposed
method outperforms comparative approaches terms PSNR while achieving superior training efficiency.
2024b) on most metrics. Qualitative comparisons in Figure 4 further show that our method produces more detailed and clearer surface structures. Additional mesh visualizations are provided in Ap- pendix B
Efficiency Comparison. We compare the training time of method with that of existing methods.
As shown in Fig. our method only takes 2 hours and 10 minutes to complete the training on the Rubble Lin al. 2022). which is significantly faster than competing methods. As presented in Table. 3] when compared with other large-scale scene algorithms, our method requires lower computational costs while achieving better rendering quality and geometric accuracy.

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Table Ablation Results on Russian dataset (Xiong et al. 2024 Bold indicates best performance.
Note that OOM denotes Out Of Memory.
Method
Rendering Quality
Geometric Quality
Training Statistics
PSNRT
SSIM
LPIPS.
Pt
R1
F1
GS (M).
Timel.
Sizel
Mem
Baseline
22.54
0.778
0.231
0.532
0.501
0.516
6.43
235
1102.23
0OM
+ST
24.68
0.816
0.188
0.518
1035.02
+LP
24.53
0.785
0.195
0.556
0.502
0.528
3.02
134
467.47
17.1
+SAPG (Ours)
24.66
0.813
0.184
0.568
0.525
0.546
2.45
122
314.24
14.4
STPG
24.57
0.801
0.201
0.563
0.511
0.536
2.73
119
320.12
13.9
Table 5: Ablation study the effects of D-Normal Regularization and Depth Consistency Reg-
ularization, conducted on the Morden Building dataset (Xiong et al. 2024). Bold indicates best performance.
Method
PSNR
SSIMT
LPIPS
FI1
w/o D-Normal
25.02
0.743
0.215
0,463
w/o Depth Consisteney
24.59
0.792
0.201
0,453
w/o Geometry-Awan Confidence
26.02
0.795
0.163
0.493
Full
26.44
0.805
0.157
0.503
4.3 ABLATION STUDIES
To validate the effectiveness of individual components in our method, we conduct a series of ablation studies on the GauU-Scene dataset. Specifically, evaluate the impact of the following compo- nents: Spatially Adaptive Gaussian Pruning (SAGP), Depth-Consistent D-Normal Regularization, and the partitioning strategy.
Ablation of SAGP& & Gaussian Partitioning As summarized in Tab. we conduct a systematic ablation to evaluate the individual contributions of our proposed SAGP and partitioning strategy. We first establish a Baseline that employs neither our SAGP nor any partitioning strategy.
For SAGP, we compare our SAGP pruning against LP, the pruning method from LightGaussian (Fan et al. 2023). The results demonstrate that our SAGP is more effective at preserving the original geometric quality (higher Fl score) while significantly reducing the number of Gaussians, training time, and memory consumption, with only minor impact on rendering quality.
For Partitioning Strategy, our full method (Ours) integrates the proposed partitioning strategy (ST) with SAGP. We further compare it against STPG, which uses the partitioning strategy from City- Gaussian (Liu et al., 2024a) with our SAGP. The comparison validates the superior effectiveness of our partitioning strategy, as it achieves better rendering and geometric quality under the same pruning method, demonstrating its ability to better preserve structural consistency across blocks.
Ablation of Depth-Consistent D-Normal Regularization. As shown in Tab. we conduct abla- D-Normal Regularization, demonstrating
tion studies on each component of the that its introduction significantly enhances both rendering quality and geometric accuracy for large- scale scenes. Quantitative results reveal consistent improvements across all evaluation metrics, with notable gains in F1-score (from 0..53 to 0.503) and PSNR (from 24.59 to 26.44), validating the critical importance of this component for high-quality large-scale reconstruction. Furthermore, as illustrated in Fig. D the Geometric Regularization substantially improves the details in rendered images, as well as the quality of rendered normal and depth maps.
5 CONCLUSIONS
This paper presents UrbanGS, scalable framework for urban-scale scene reconstruction. It in- troduces depth-consistent D-Normal regularizer that enables comprehensive optimization of all Gaussian geometric parameters by fusing depth and normal cues. A spatial pruning strategy and seamless partitioning further enhance efficiency and avoid artifacts. Experiments show UrbanGS outperforms existing methods in rendering, geometry, and training speed, offering practical solu- tion for large-scale 3D reconstruction.
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ACKNOWLEDGEMENTS
This work was supported by the National Key R&D Program of China (Grant No. 2024YFC3308200), the Key R&D Program of Henan Province of China (Grant No. 231111211500), and the National Natural Science Foundation of China (Grant No. 62406298). The experiments part was supported by the Ministry Economic Development of the Russian Fed- eration (agreement identifier 10000CC13922P3XX 17, 2025).
Grant No. 139-15-2025-000 dated April
ETHICS STATEMENT
This work presents UrbanGi,, a scalable framework for high-fidelitg large-scale scene reconstruc- tion. The research focuses on methodological innovation address challenges geometric con- sistency, memory efficiency, and computational scalability of 3D Gaussian Splatting in urban-scale applications. All experiments use publicly available benchmark datasets (Mill-19, UrbanScene3D, GauU-Scene) in line with academic practices, involving no human subjects, personal data, or so- cial risk assessment. The authors encourage ethical and legal use of this technology and declare no potential confficts of interest.
REPRODUCIBILITY STATEMENT
To ensure reproducibility of UrbanGS's results, we provide key details: the proposed components (Depth-Consistent D-Normal Regularization, Spatially Adaptive Gaussian Pruning, partitioning strategy) are detailed in the methodology section with mathematical formulations Experimental setups include training 2.0+, Open3D 0.18.0+, and pretrained models (DepthAnything--2 Dsine) Dataset preparation follows the image downsampling strategy (resizing images wider than 1600 pixels) and original rrain/validation splits. We will make the complete code and training scripts publicly available on GitHub upon the final revision and acceptance of this paper.
NVIDIA RTX A5000 GPUs (baselines RTX A800), using PyTorch
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Yuqi Zhang, Guanying Chen, and Shuguang Cui. Efficient large-scale scene representation with hybrid of high-resolution grid and plane features. Pattern Recognition, 158: 1001, 2025.
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A IMPLEMENTATION DETAILS
Training Setup. UrbanGS are trained using NVIDIA A5000 GPUs, while all baseline methods are trained on NVIDIA A800 GPUs. Since the Mill-19 Yuu et al., 2022), UrbanScene3D (Lin et al.) 2022). and GauU-Scene (Xiong et al. 2024) datasets contain thousands of high-resolution images, we follow the image downsampling strategy proposed in 3DGS: any image with a width exceeding 1600 pixels is resized proportionally during both training and validation. geometric priors, utilize the DepthAnything-vv model (Hu et al. (024)) for depth prediction and the pre-trained Dsine model (Bae & Davison 2024 for surface normal estimation.
Regarding the pruning schedule, our design follows the training dynamics of 3DGS and prior prac- tice. As shown the pipeline use stages of pruning. When constructing the coarse global Gaussian model, we apply an initial, simple pruning rule to remove obviously redundant Gaussians, reduce memory, and obtain compact global prior for subsequent block-wise training. During block refinement, we prune at 7k, 15k, and 25k iterations (out of 30k). The 7k step is applied after the scene has roughly formed and the Gaussian distribution starts to stabilize, consistent with the behav- ior observed in 3DGS (Kerbl et al. 2023), and removes early exploratory Gaussians that no longer contribute to the final geometry. The 15k step follows the original 3DGS setting, occurring at the end of densification when the Gaussian count peaks, and is most effective for controlling model complexity and overfitting. The final pruning at 25k, inspired by LightGaussian (Fan et al. 2023). acts consolidation step near convergence, further eliminating residual redundancy and ensuring a good balance between figh-fidelity reconstruction and compact, efficient rendering.
Mesh Extraction. To obtain the final mesh, employ Open3D's volumetric TSDF fusion method, which integrates rendered depth maps and corresponding camera poses to construct continuous Signed Distance Field (SDF). The surface is then extracted using the Marching Cubes algorithm at the zero-level isosurface, enabling direct reconstruction of 3D geometry without relying on interme- diate point cloud representations.
B PROOF ON A DEPTH- CONSSSCONT D-NORMAL REGULARIZER
These propositions systematically validate the evolutionary process from traditional rendered normal supervision to our proposed depth-normal regularizer. Proposition 1.1 reveals the limitation of su- pervising only rendered normals in updating Gaussian positions; Proposition 1.2 demonstrates that the depth-normal regularizer can effectively optimize Gaussian positions; Proposition 2. further proves that the depth-consistent regularizer significantly improves geometric accuracy, highlighting the enhanced effectiveness of our method.
B. GEOMETRIC PROPERTIES
To reconstruct the 3D surface, we focus on the geometric properties of Gaussians that enable accu- rate intersection depth calculation, as detailed below.
Normal Vector Following NeuSG (Chen et al. 2023), the Gaussian's normal vector n E T is defined as the direction of its minimized scaling factor:
n R(k, :]. k = arg min ([s1, 82, sa])
(16)
Both n and Gaussian center are transformed to the camera coordinate system (default unless stated otherwise).
Intersection Depth Existing work (Tang et al. 2024a) uses p for depth calculation, which is in- accurate (depth unrelated to n). We instead compute the ray-Gaussian intersection depth via the following steps:
Gaussian Flattening with Scale Regularization: To simplify intersection computation, we adopt NeuSG's Chen et al. 2023) scale loss to flatten 3D Gaussian ellipsoids into planes (p,
C. |mmin (51, $2, s3)ll
(17)
This loss constrains the minimum scaling factor component approach zero.
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Moving Direction
Normal Direction
Gaussian
Moved Gaussian
Position
GT Surface
GT Surface
N
Position2
Position2
Ray Direction
Position]
Position!
Figure A: Illustration of Proof of the Proposition Comprehensive Update of Gaussian Parameters. (a) After back- back-propagation through alpha-blose blending Eq. [ the rendered normal supervision loss En. moves Gaussians either closer to (corresponding Positiong) the intersecting ray. When the normal of a Gaussian is closer to the ground truth (GT) surface normal, this supervision mechanism pushes the Gaussian (e.g.. Position,) toward the ray increase its weight in the rendering equation; conversely, if there is a significant deviation between the two normals, pushes the Gaussian (e.g.. Posiliona) away from the ray. (b) In contrast, the D-Normal regularizer loss Ldn can move Gaussians either closer to or farther from the GT surface. Here, Position, and Positiong are 3D positions corresponding to the mean depth of two adjacent pixels (rays), computed via Eq. 5 the D-Normal N, is derived from Position, and Position2 using Eq. 66. Notably, f relies on the intersection depth, related to Gaussian position Position and normal n) to encourage Na alignment with the GT normal N, ultimately enabling Gaussians to move toward or away from the (GT) surface.
Position1) or farther from (corresponding to
For the plane constraint, any point Op on plane (p, (p,n) n) satisfies (0p p) = O. For the ray representation, ray originating from the origin is expressed or rt, where denotes the ray direction and / is the distance from the origin. At the intersection (or = 0p), solving for the depth along the camera z-axis yields:
n.P
d(n, p) =r= n.r
(18)
where is the z-component r.
This d(n, p) correlated with both p and n, ensuring accuracy and enabling D-Normal regulariza- tion to backpropagate loss to Gaussian parameters.
B.2 PROOF PROPOSITIONS
Supervising the rendered normals cannot effectively influence the positions of Gaussians. The rendered normal N is defined as the opacity- average of Gaussian nor- mals. Considering the normal loss L.,, its gradient with respect to the Gaussian position p; can be expressed via the chain rule as:
Proposition 1.1
Ocn
ac,
ON Op:
(19)
0p:
ON
On; = 0. Thus, the Since each Gaussian normal n; determined solely by the rotation parameters, dp. dependency of Ñ on p. originates only from the opacity weights a;:
OÑ = da; aG(x) 0p,
ON da: 0G(x).
(20)
0p:
For Gaussian distribution
G(x) exp
( p4) T 1 pi))
(21)
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obtain
0G(x) Öp:
G(x) E -
p.).
(22)
In our implementation, following scale regularization, each Gaussian is flattened into an approxi- mate plane, so we approximate Σ-1 by the identity matrix emphasize directionality. Hence,
oG(x) G((x) (x pc).
(23)
Öp;
Substituting into Eq.(16), the resulting position gradient is
dCn Op;
(xr pi).
(24)
This indicates that the position update depends only on the spatial offset between the pixel-aligned point æ and the Gaussian center Pa. without involving the surface normal n4, Consequently, con- ventional normal supervision can only adjust opacities but fails to drive positions toward the true surface along its normal direction. This explains why rendered-normal supervision alone leads to incomplete geometric optimization.
Proposition 1.2 Supervising our proposed Depth-Normal (D-Normal) regularizer can effectively influence the positions of Gaussians.
We now consider our proposed D-Normal loss Ldn. By definition, the D-Normal Na computed from the gradients of rendered depth maps. The gradient of Lan with respect to the Gaussian position p: follows a staee chain rule:
dL.r 0p:
0Cnn
ON. aD
(25)
ON
a
0p:
where D denotes the rendered depth.
Since 1 is the opacity-weighted average of Gaussian intersection depths d; its derivative can be decomposed as:
aD
a
dos
aD od,
(26)
Öp:
0a:
Öp.
Odg
ap.
(A) Conventional weight term
(B) Depth term (now)
The depth of Gaussian-ray intersection is given by:
d, = T2 n2 Ps n2 T
(27)
where r is the viewing ray and r= its z-component. Differentiating with respect ps yields:
Od,
r2 n.
(28)
Op:
Thus, the second term (B) in 0D/0p, explicitly involves the surface normal n;. we obtain:
0L.m
0L.
ON, a aD da, OG(x)
aC.. aN, DD n,
0p:
ON.
aD da, 0G(x) 0p:
ON a 0d,
To n4
(29)
naditional weight-dependent
proportional vi;
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The new term proportional to n, provides direct mechanism to update the position p; along the normal direction. As a result, D-Normal supervision not only influences Gaussian rotations (as conventional normal supervision does) but also effectively aligns Gaussian positions with the un- derlying surface geometry. This theoretical insight explains the substantial geometric improvements observed in experiments.
Proposition 2.1
Supervising our proposed Depth Consistent D-Normal Regularizer, which incorporates the Pseudo Depth & D-Normal Dual Supervision Mechanism by utilizing both D-Normal maps and pseudo depth maps, can effectively and stably influence Gaussian positions along the normal direction, thereby achieving comprchensive updates of geometric parameters (rotation and position) and sig- nificantly improving geometric and reconstruction accuracy.
From Proposition 1.1, conventional rendered normal supervision provides gradients 0Cn (p-ps), which independent of Gaussian normals n4 and thus fail to guide positions toward the true surface.
0p,
From Proposition 1.2, D-Normal supervision introduces an additional term aD n4
den
den
ON
(30)
0p:
ON
aD
0d,
which is explicitly proportional to the normal n4. This enables position updates along the surface normal direction, thus coupling position and rotation optimization.
However, the reliability of this update depends on the accuracy of rendered depth D. To further enhance stability, we introduce pseudo depth supervision (D, Deseudo). Its gradient contributes
OLpD
aLpD aD
(31)
Op.
YD
0p:
which shares the same structural dependence on ab the D-Normal term, and therefore reinforces the normal-dependent component introduced above.
öp.
Combining these two complementary signals, the dual supervision mechanism (i) stabilizes depth estimation via pscudo depth, and (ii) ensures consallconsiseett position updates via D-Normal. As result, both rotation and position parameters of Gaussians are comprehensively optimized, yielding improved geometric accuracy in reconstruction.
posiitiooonooiiiin
C SUPPLEMENTATION TO THE PARTITIONING STRATEGY
Existing large-scale 3DGS frameworks exhibit two critical limitations: geometric discontinuities block boundaries introduce visible fusion artifacts, while redundant Gaussians attract contributing views that inflate computational loads during block-wise training. Our unified approach addresses both issues through integrated redundancy reduction and geometric continuity enforce- ment.
The pipeline begins with global pruning of low-impac Gaussians using spatially adaptive scoring (Eq. 15
pruned G3 E G S > 0 ppnne:
(32)
This operation targets background and fow-contribution primitives that attract irrelevant views. Pre- partition pruning eliminates redundancy propagation local blocks, significantly reducing compu- tational load.
To enable spatially balanced partitioning, the pruned Gaussians are contracted into a normalized cube [] using hybrid contraction function (as in (Wu et al. 2023)), which applies a lin- ear mapping to the foreground region and a nonlinear scaling to the unbounded background. This contraction yields a compact representation of the full scene and facilitates uniform space division.
Within this contracted space, the scene partitioned into regular blocks. To preserve geometric continuity across adjacent partitions, boundary Gaussians explicitly duplicated:
G Shured
(Gx (is AB,) 5 }
(33)
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This duplication enforces overlapping geometric constraints near block interfaces, thereby suppress- ing boundary artifacts during block-wise wise fusion.
Camera pose assignment for each block Bj integrates geometric proximity and perceptual contri- bution through dual evaluation criteria. The geometric criterion assesses physical containment by checking if the contracted camera position pr, contract(p..) falls within the block's spatial extent (b min: max)), formalized as:
{1
Pr, E [b).iin bj.max)
Byea (Tc)
(34)
0
otherwise
where the contraction operator follows (Liu et al. 2024a).
The perceptual criterion quantifies visual degradation when removing Gaussians Gg,- By compar- ing renders التا (full model) and resck- (excluding G3,) in original space, computes:
Beis(T,) {
SSIM( ( rexel-) ej
(35)
otherwise
with Ej controlling sensitivity to structural loss, identifying perceptually dependent poses.
The final assignment combines both criteria:
B(T() (T)) V Bris(Ts):
(36)
ensuring each pose is assigned to blocks physically occupies or visually relies upon. This estab- lishes efficient view-block correspondence while maintaining rendering consistency.
Table A: Comparison of training times across multiple ofie-o-ft-hhrrttt methods on the Mill-19 Yu
al. 2022 and UrbanScene3D [Lin al. 2022). Bold indicates best performance..
Models
Building Time +
Rubble
Residence
Sci-Art
Time &
Time $
Time +
Mega-NeRF
19:49
30:48
27:20
27:39
Switch-NetB
24:46
38:30
35:11
34:34
VastGS
03:26
02:30
03:12
03:13
DOGS
03:51
02:25
04:33
04:23
CityGS-v2
04:25
03:05
04:45
(4::88
Ours
03:13
02:10
02:45
03:40
Table B: Novel View Synthesis Performance Evaluation on the GauU-Scene datasets (Xiong et al. 2024..
Bold indicates best performance.
Residence
Russian Building
Modern Building
Methods
SSIM
PSNR t
LPIPS 1
SSIM 1
PSNR
IPIPS
SSIM
PSNR
LPIPS J
NeuS
0.244
15.16
0.674
0.202
13.65
0.694
0.236
14.58
0.694
Neuralangelo
NaN
NaN
NaN
0.328
12.48
0.698
NaN
NaN
NaN
SuGaR
0.612
21.95
0.452
0.738
23.62
0.332
0.700
24.92
0.381
GOF
0.652
20.68
0.391
0.713
21.30
0.322
0.749
25.01
0.286
VCR-Gaus
0.663
22.69
0.404
0.724
22.89
0.273
0.726
25.19
0.230
2DGS
0.703
22.24
0.306
0.788
23.77
0.189
0.776
25.77
0.202
CityGS-v2
0.742
23.57
0.243
0.784
24.12
0.196
0.770
25.84
0.207
0.762
0.206
24.53
0.158
26.44
D
MORE EXPERIMENTS
The experimental section of this paper focuses on evaluating the performance UrbanGS in large- scale scene reconstruction. Through comprehensive comparisons with variety of baseline methods,
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Table C: Detailed geometry evaluation on GauU-Scene datasets (Xiong al. 2024). "NaN" indi- cates invalid numerical results, while "FAIL" denotes failure to extract valid mesh For all metrics, indicates higher values are better.
Campus
Village
College
Methods
P
R
F1
P1
R
FI
P1
R
FI 1
NeuS
FAIL
FAIL
FAIL
FAIL
FAIL
FAIL
FAIL
FAIL
FAIL
Neuralangelo
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
SuGaR
0.321
0.272
0.294
0.354
0.253
0.295
0.409
0.271
0.326
VCR-Gaus
0.478
0.312
0.379
0.492
0.412
0.448
0.456
0.361
0.401
2DGS
0.389
0.304
0.341
0.442
0.283
0.345
0.340
0.182
0.237
GOF
FAIL
FAIL
FAIL
FAIL
FAIL
FAIL
FAIL
FAIL
FAIL
PGSR
0.464
0.355
0.403
0.535
0.445
0.486
0.349
0.349
0.354
CityGS-X
0.505
0.361
0.421
0.545
0.443
0.489
0.559
0.371
0.446
CitGaassian
0.486
0.383
0.428
0.580
0.503
0.543
0.577
0.373
0.453
Ours
0.492
0.388
0.435
0.567
0.512
0.538
0.564
0.381
0.456
Table Ablation study of different priors on the Modern Building dataset.
Priors
Rendering Quality
Geometric Quality
Dav2
MiDaS
Dsine
GeoWizard
SSIM
PSNR f
LPIPS
P1
R
Fl
0.805
26.44
0.157
0.663
0.404
0,503
0.802
26.48
0.163
0.399
0.498
0.798
26.33
0.161
0.645
0.410
0.501
V
0.785
26.12
0.166
0.658
0.392
0.491
the effectiveness of UrbanGS is validated across three key aspects: training efficiency, novel view synthesis quality, and geometric accuracy.
D. TRAINING EFFICIENCY
As shown in table A in training time comparison experiments conducted on diverse scenes such Building, Rubble, Residence, and Sci-Art, UrbanGiS demonstrates significant efficiency gains. For instance, in the Building scene (Yu al., 2022), UrbanGS completes training in only 3 hours and 13 minutes, substantially outperforming Mega-NeRU (19 hours 49 minutes) (Turki et al. (022) and Switch-NeRF (24 hours 46 minutes)
(Mi & Xu 2023). Even when compared with more efficient
baselines such as VastGS (Lin et al. 2024), UrbanGS consistently achieves competitive or superior training times across most scenes.
Table E: Effect block partitioning on Russian datasct. Memory (Mem) in GB, Time minutes. Bold indicates hest performance.
Block/GPU
PSNR1
SSIM
LPIPS
Fif
Mem.
Time!
2/2
23.43
0.779
0.215
0.518
25.2
170
4/4
24.55
0.804
0.201
0.539
20.1
140
8/8
24.66
0.813
0.184
0.546
14.4
122
D.2 NOVEL VIEW SYNTHEIS
As shown in table the novel view synthesis performance is also evaluated on the GauU-Scene dataset Xiong et al. 2024), UrbanGS outperforms competing methods in all tested scenes using SSIM, PSNR (higher is better), and LPIPS (lower is better) as evaluation metrics. Specifically, in the Residence scene, it achieves SSIM 0.762, PSNR 23.78, and LPIPS 0.206; in the Russian
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Building scene, SSIM 0.810, PSNR 24.53, and LPIPS 0.158; and in the Modern Building scene, SSIM 0.805, PSNR 26.44, and LPIPS 0.157. These results consistently surpass baselines such as SuGaR (Guédon & Lepetit 2024) and GOF (Yu et al. 2024b).
D.3 GEOMETRIC ACCURACY
To further assess the generalization of our method large-scale urban scenes, we additionally eval- uate geometry quality on the GauU-Scene dataset (Xiong et al. 2024). As summarized in Table C we report precision (P), recall (R), and Fl score across three subsets (Campus, Village, and College). Several NeRF- and 3DGS-based baselines either produce invalid numerical results ("NaN"") or fail to extract a valid mesh ("FAIL.") highlighting the difficulty of this benchmark. In contrast, our method consistently reconstructs valid meshes and achieves the best highly competitive performance all metrics. As a qualitative complement to the numerical results, Figure B and Figure C compares the reconstructed meshes of representative methods multiple GauU-Scene subsets. Baseline ap- proaches often suffer from over-smoothed surfaces, broken structures, or missing fine-scale details, particularly around building facades and road layouts, whereas method produces more complete and coherent geometry with sharper boundaries.
D.4 MORE ABLATIONS
As shown in Figure D. we conducted ablation studies on the Campus dataset. When the Depth- Consistent D-Normal Regularization module and Partitioning Strategy module are ablated, signif- icant differences are observed in both rendered normal maps and rendered depth maps compared with our full method, demonstrating the substantial effectiveness of these modules the proposed approach. To more clearly demonstrate the effectiveness of our module in pushing 3D points along the normal direction, we conduct an experiment on a small scene. As shown in Figure E. when the Depth-Consistee D-Normal Regularization module is removed, the point cloud object surfaces becomes highly scattered. With our regularizer enabled, the points are driven toward the underlying surfaces, resulting in much more compact point cloud and significantly cleaner reconstruction.
Table D studies the influence of using different depth estimators (Dav2 Hu al. 2024), MiDaS
(Ranftl et al.) 2020)) and the normal prior (Geo WieoWizard (Fu et al. (024). Dsine (Bae & Davison 2024 the Modern Building dataset. Across all combinations, the rendering metries remain very close (SSIM R 0.79-0.81, PSNR ~ 26. 1-2655, LPIPS x 0.157-0. 166) and the geometric quality (P/R/F1) only fluctuates within small range. This indicates that our framework is not sensitive to the particular choice of depth or normal prior. Thanks to the explicit depth-consistency and normal-consistency constraints, the optimization can effectively correct the bias of different priors and consistently recover high-quality geometry.
As shown in Table under the experimental scenario of the Russian dataset, this table presents the impacts of different "Block/GPU count" configurations on model performance, memory con- sumption, and training time. As the configuration is scaled up from 2/2 (2 blocks, GPUs) to 8/8 (8 blocks, 8 GPUs), the model performance is gradually optimized: PSNR increases from 23.43 to 24.66. SSIM rises from 0.779 to 0.813, LPIPS decreases from 0.215 to 0.184, and FI improves from 0.518 to 0.546. Mcanwhile, resource consumption is significantly reduced, with memory usage dropping from 25.2 GB to 14.4 GB and training time shortening from 170 minutes to 122 minutes. This demonstrates the positive role of the parallel training strategy- where the number of blocks matches the number of GPUs-in balancing sarforrannce-effciineyy
Table G reports the effect of removing individual components in our block partition strategy on the Russian scene of the GauU-Scene dataset (Xiong et al., 2024). Starting from our full model, discarding the global pruning term in Eq. 32 leads to a clear increase in the number of Gaussians (2.45M + 3.01M), longer training time and higher memory usage, together with a slight drop in both rendering and geometric quality. This confirms that the spatially adaptive pruning not only reduces redundancy but also facilitates optimization. Removing the Eq. 33 also degrades SSIM, LPIPS, and F1, indicating that sharing Gaussians across neighboring blocks is important for suppressing boundayy artifacts. When the geometric pose- aose-assigmment criterion in Eq. 34 is disabled, the performance drops most significantly (e.g., PSNR and F1 both
rule in
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Campus
Russian
Morden
Village
VCR-Gar Ga-s
2DGS
GOF
PGSR
CSyySS-2
OHrs
Figure B: Visual comparison of meshes from state-of-the-art (SOTA) methods.
decrease considerably) while the computational cost increases, showing that aligning camera frusta with physically relevant blocks is crucial for both fidelity and efficiency. Finally, removing the perceptual criterion Eq. 35 causes moderate decline in rendering quality and F1, demonstrating that perceptual filtering helps retain poses that are visually important for each block. Taken together, these results show that all components of our partition strategy contribute to the overall trade-offf, and the full design achieves the best balance between reconstruction quality and resource consumption.
As shown in Figure G on the Sci-Art scenes (Lin et al. 2022) we observe that 3DGS-based meth- ods with explicit geometry optimization often yield lower rendering quality than the original 3DGS. These scenes contain many aerial-style images dominated by distant sky regions with weak or am- biguous geometry. In such backgrounds, pearance, producing coarse color blotches and unnatural boundaries the rendered views. con- trast, although the original 3DGS is also imperfect in sky modeling, its results still vaguely preserve
-optiirr-optimized variants tend to degrade the sky ap-
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Campus
Village
Morden
2DGS
GoF
VCR-GauS
PGSR
CityGS-x
CityGSvv2
Ours
Figure C: Qualitative mesh and texture comparison between SOTA and our method on the Campus, Village, and Morden Buliding scenes (Xiong et 2024).
wio Daprih-Consistett D-Normal Regufarization
wro Partitioning Strategy
Figure D: Ablation experiments on the Campus Dataset Lin al. 2022
cloud layers and building silhouettes. This discrepancy highlights limitation of current geometry optimization objectives when applied to background regions lacking clear geometric structure.
Table H presents the ablation study results of the two key hyperparameters. Va and in the geometry- aware confidence mechanism, conducted on the Modern Building (Lin et al. (022). scene. The baseline configuration (7a 0.1, 0.01) achieves the optimal performance across all evaluation
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Figure E: Qualitative ablation for the Depth-Consistent D-Normal Regularizer. We visualized the centers of Gaussian ellipsoids in 3D scene. In the left figure, the Depth-Consistent D-Normal Reg- ularizer is disabled, while the right figure demonstrates the results with our proposed regularization. In comparison, the left figure exhibits a notable number of Gaussian ellipsoids floating off the sur- face. Our proposed Septh-Consistent D-Normal Regularizer effectively pushes the 3D Gaussians toward the surface, resulting in a cleaner reconstruction.
Table F: Quantitative Analysis of Weight Configuration Ablation Study
Weight Configuration
PSNR
SSIM
LPIPS
F1 f
(1.2, 1.0, 0.8)
26.44
0.805
0.157
0.503
(1.0, 1.0, 1.0)
26.19
0.791
0.172
0,487
(0.0, 1.0, 1.0)
24.32
0.763
0.215
0.432
(1.0, 0.0, 1.0)
25.12
0.778
0.198
0,468
(1.0, 1.0, 0.0)
25.87
0.793
0.169
0.485
(1.2, 1.0, 1.0)
25.95
0.782
0.185
0.451
(Grund Trunk)
(1.. 03)
(...1... 13)
(0.6. 1.. 1.6)
[1.. 0.6)
(10, 1D)
Figure F: Ablation experiments on the Morden Building Dataset Xiong et al. (2024)
metrics, with PSNR of 26.44 and Fi-score of 0.503. Decreasing "a to 0.05 alone leads to noticeable performance degradation (PSNR drops by 0.32, F1-score drops by 0.018), indicating that excessive sensitivity to depth gradient consistency suppresses valid geometric signals. Similarly, increasing to 0.02 alone also causes performance deterioration (PSNR drops by 16, F1-score drops by 0.01 suggesting insufficient suppression of depth errors adversely affects reconstruction quality. The worst performance occurs when both parameters are modified (74 = 0.15, T = 0.005), with PSNR and F1-score decreasing by 0.55 and 0.025 respectively, validating the coupling relationship between the two hyperparameters and the rationality of the baseline selection. These results comprehensively demonstrate that chosen hyperparameter combination achieves the optimal balance between geometric consistency and error suppression.
D.5 LINEAR WEIGHTED PRUNING
We refer to the conventional weighting scheme as Linear Weighted Pruning (LWP), where the impor- tance score is computed linear combination of the three attributes: S.WPP a- +3 -- Wy, To analyze the impact of the weighting hyperparameters, we conduct an ablation study on the LWP formulation. As summarized in Table F we systematically vary a, B, and and evaluate the
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Table G: Ablation Results of Block Partition Strategy on Russian Scene Dataset (iiong et al,
2024 Bold indicates best performance.
Rendering Quality
Geometric Quality
Training Statisties
Method
PSNRT
SSIM
LPIPS]
P
RT
Fift
GS (millions))
Time (min))
Size (MB))
Mom (GB))
Efffct of Removing Individual
Compunents
(ours)
bascline w/o Eq,
32
24.43
0.797
0.201
0.562
0.518
0.539
3.01
142
429.41
17.5
baseline wro Eq.
33
24.51
0.802
0.198
0.564
0,513
0.537
2.44
129
314.24
15.1
baseline Eq
157
buseline w/o Eq
24.42
0.808
0.188
0.566
0.521
0.543
2.46
125
314.31
14.7
Table H: Ablation Study of Geometry-Aware Confidence Hyperparameters
Hyperparameter Settings
PSNR F1-score
Baseline (Ta 0.1, 0.01)
26.44
0.503
Only 7a = 0.05
26.12
0,485
Only 0.02
26.28
0.492
Both modified (Ya = 0.15, r 0.005)
25.89
0.478
Ground Truth
Ours
3DGS
CityGS-v2
PGSR
VCR-Gaus
Figure G: Qualitative mesh and texture comparison between SOTA and our method on Art-Sci Scene (Ln et al. 2022).
sulting rendering quality (PSNR, SSIM, LPIPS) and geometric accuracy (Fl-score). The optimal configuration (a = 1.2, 3 = 1.0, 0.8) achieves the best performance across all metrics: PSNR 26.44, SSIM 0.805. LPIPS 0.157, and F1-score 0.503. Compared an equal-weight baseline (a=3=9- = = 1.0), this setting improves PSNR by 0.55 dB and F1-score by 0.016.
Univariate ablation reveals the distinct roles of each term: setting a 0.0 (removing the ray-intersection frequency) causes 14.1% drop in F1-score, underscoring its importance for multi-view consistency; setting / 0.0 (ignoring opacity) increases I.PPS by 26.1 %, indicat- ing direct impact on visual quality; and using 7 1.0 (i.c., a linear volume weight instead of the sub-linear one) degrades all metrics, confirming that aggressive volume weighting introduces exces- sive redundancy. Thus, the chosen weights strike balance between rendering fidelity, geometric completeness, and computational efficiency.
Qualitative results in Figure F show that rendered views remain visually consistent across different weight combinations, with no catastrophic failures even for suboptimal settings. This demonstrates that the LWP scheme is reasonably robust moderate variations in a, B, provided they stay
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within sensible range. Nevertheless, the need for manual hyperparameter tuning in LWP motivates our proposed multiplicative scoring (Eq. 13), which eliminates such tuning while preserving or improving performance.
E LIMITATIONS
Although UrbanGS demonstrates advantages in large-scale reconstruction, it still exhibits certain limitations. Its geometric regularization relies on monocular depth/normal priors derived from pre- trained networks, which may propagate estimation errors into the reconstruction reconsructionnppariicuullt particularly in regions with weak textures or extreme lighting conditions. Additionally, the method primarily fo- cuses static environments and does explicitly model dynamic objects commonly found in urban scenes. Future work will aim to mitigate dependency on monocular priors through multi- view geometric consensus and extend the framework dynamic urban objects via explicit motion modeling.
F USE OF LARGE LANGUAGE MODELS
A large language model (LLM) was used solely for language-level assistance, such as improving readability, fluency of the text and formatting ылІ tables and retrieve related works. The research ideas, experiments, and results are entirely the work of the authors, who bear full responsibility for content of this submission.
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Reviews:
[{"review_id": "du3RZKnWQY", "reviewer": "Reviewer_iVxm", "summary": "This paper addresses the challenge of scalable large-scale 3D reconstruction using 3D Gaussian Splatting (3DGS). The authors aim to improve both geometric accuracy and model efficiency. To this end, they propose two main technical contributions: 1) a Depth-consistent D-Normal Regularization term to enforce geometric fidelity, and 2) a Spatially adaptive Gaussian Pruning strategy to optimize the model's compactness. The authors present quantitative and qualitative comparisons against relevant baselines, supported by ablation studies, to demonstrate that their proposed method achieves more geometrically accurate reconstructions while maintaining a lightweight model.", "questions": "1. **Experimental Comparison:** Following the point on related work, please provide quantitative and qualitative comparisons against CityGS-X [1] on at least one shared dataset.\n2. **Sensitivity to Priors:** How sensitive is the proposed method to the quality and noise level of the monocular priors? We request that the authors demonstrate the robustness of the confidence weighting (Eqs. 9–11), for instance by evaluating with different depth/normal estimators or by analyzing performance after injecting synthetic noise into the priors.\n3. **Contribution of Depth-Consistency Term:** It is unclear if the performance gain from the depth-consistency term (Eq. 8) is due to the novel formulation itself or primarily due to the strength of the chosen prior (DepthAnything-v2 [2]). Please provide an ablation study \n that uses alternative estimators (e.g., MiDaS[3], DPT[4]) to isolate the contribution of the regularization term.\n4. **SAGP Hyperparameter Analysis:** To better understand the design of the SAGP component, please provide sensitivity analyses for its key hyperparameters (e.g., the weights $\\alpha, \\beta, \\gamma$ in Eq. 15; $\\kappa$ in Eq. 14; the cell size in Eq. 13; and the pruning schedule).\n\n---\n\n[1] Gao, Yuanyuan, et al. \"Citygs-x: A scalable architecture for efficient and geometrically accurate large-scale scene reconstruction.\" arXiv preprint arXiv:2503.23044 (2025).\n\n[2] Yang, Lihe, et al. \"Depth anything: Unleashing the power of large-scale unlabeled data.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2024.\n\n[3] Birkl, Reiner, Diana Wofk, and Matthias Müller. \"Midas v3. 1--a model zoo for robust monocular relative depth estimation.\" arXiv preprint arXiv:2307.14460 (2023).\n\n[4] Ranftl, René, Alexey Bochkovskiy, and Vladlen Koltun. \"Vision transformers for dense prediction.\" Proceedings of the IEEE/CVF international conference on computer vision. 2021.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The paper is well-written and clearly organized, contributing to an effective presentation of the proposed method.\n- The proposed method outperforms SOTA baselines across key metrics (memory consumption, geometric fidelity, novel view synthesis) and achieves visually cleaner mesh extractions on standard benchmarks (Figs. 3–4).", "weaknesses": "1. **Missing Related Work:** The related work section appears to omit a discussion of recent, relevant methods in large-scale 3D Gaussian Splatting, most notably CityGS-X [1]. Please discuss this work and position the current method in relation to it.\n2. **Incremental Novelty:** The paper's conceptual novelty appears to be limited. The core components, such as the D-Normal regularizer and the partitioning strategy, seem to be extensions of prior art (e.g., VCR-Gaus [2] for consistency, CityGaussian/CityGS-v2 [3] for partitioning). The authors should more clearly articulate the core conceptual contributions of their work beyond the successful integration and extension of these existing ideas.\n3. **Clarity of Depth Consistency:** The explanation for the depth consistency regularization requires clarification. To assess the method, please elaborate on how multi-view consistency is enforced. Specifically:\n    - To which view(s) are the Gaussian depths rendered?\n    - How is the depth loss calculated between these views?\n4. **Motivation and Validation of SAGP:** The formulation of the SAGP component combines several known heuristics (e.g., volume, opacity). The specific weighting in Eq. 15 lacks theoretical motivation.\n---\n[1] Gao, Yuanyuan, et al. \"Citygs-x: A scalable architecture for efficient and geometrically accurate large-scale scene reconstruction.\" arXiv preprint arXiv:2503.23044 (2025).\n\n[2] Chen, Hanlin, et al. \"Vcr-gaus: View consistent depth-normal regularizer for gaussian surface reconstruction.\" Advances in Neural Information Processing Systems 37 (2024): 139725-139750.\n\n[3] Liu, Yang, et al. \"Citygaussianv2: Efficient and geometrically accurate reconstruction for large-scale scenes.\" arXiv preprint arXiv:2411.00771 (2024).", "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": "5IwljEqpZM", "reviewer": "Reviewer_oxb3", "summary": "The paper proposes **UrbanGS**, a 3D Gaussian Splatting–based framework targeting large-scale urban scene reconstruction with accurate geometry, memory efficient, and scalability\n\nIts main contributions are:\n\n1. **Depth-Consistent D-Normal Regularization** — combines rendered normal supervision with depth guidance from a monocular estimator to jointly optimize Gaussian position, rotation, and scale.\n2. **Geometry-Aware Confidence Weighting** — adaptively down-weights unreliable regions based on gradient consistency and inverse-depth deviation.\n3. **Spatially Adaptive Gaussian Pruning (SAGP)** — prunes redundant Gaussians based on local geometric complexity and opacity.\n4. **Partitioning strategy** for large-scale training with reduced boundary artifacts.\n\nExperiments across Mill-19, UrbanScene3D, and GauU-Scene datasets show that UrbanGS improves novel-view synthesis and geometry reconstruction performance, and reduces memory/time costs compared to CityGS-v2,VCR-Gaus, and other NeRF-based and GS-based reconstruction methods.", "questions": "1. How does the proposed method avoid scale ambiguity inherent to monocular depth? Can you show quantitative depth alignment across views (e.g., reprojection error or cross-view consistency metric)?\n2. How does the proposed D-normal regularization differ from existing methods from 2DGS[1] and others[4][5]?\n3. In Eq. (14), please define $v_i, v^{(t)}_{local}$, and explain how the percentile $t$ is chosen.\n4. It would be great to show some justification (e.g.,ablation on design) for the $L_{id}$ of Eq.(8) and geometry-aware confidence of Eq.(11).\n5. Sci-Art results underperform simpler baselines; it would be great if the authors analyze why.\n6. It would be great if the authors to provide more qualitative results on benchmarks datasets, especially for GauU-Scene.\n7. How sensitive is the method to the many hyperparameters? Could a fixed default generalize? More ablation studies (quantitative & qualitative) on these hyperparamaters may be necessary to support the scalability of the work.\n8. Please clarify if the “position update along normal” proof was verified empirically (e.g., by measuring displacement vectors relative to normals).\n\n[4] Liang, Zhihao, et al. \"Gs-ir: 3d gaussian splatting for inverse rendering.\" *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*. 2024.\n\n[5] Chen, Hongze, Zehong Lin, and Jun Zhang. \"Gi-gs: Global illumination decomposition on gaussian splatting for inverse rendering.\" *The Thirteenth International Conference on Learning Representations*.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- Consistent experimental setup and relatively solid quantitative evidence seen throughout the tables.\n- Strong implementation efficiency; runs faster and more memory efficient than CityGS-v2\n- Major ablation studies supporting participation of proposing mehtods.", "weaknesses": "1. **Limited novelty** – D-Normal regularization with depth cues closely resembles 2DGS[1]; little conceptual advancement beyond combining two existing signals.\n2. **Unclear multi-view consistency claim** – depth anchors from monocular estimators cannot guarantee scale-aligned supervision across views; no metric (e.g., depth reprojection error) verifies this. Recent works apply learnable scaling factors[2] or video-diffusion models[3] to deal with the multi-view consistency, but current work lacks such effort.\n3. **Geometry-aware confidence** – seems a heuristic way to use the reciprocal of the depth. No validation exists to justify the design of $L_{id}$\n4. **Unclear pruning formulation** – lack definition; reproducibility depends on unspecified percentile $t$ of Eq.(14).\n5. **Moderate increase in qualitative results** — View are not consistent (Fig.3, Fig B), hard to compare the qualitative results. Also, without ground truth, it is hard to say that current visualization show enhanced reconstruction (e.g., for Figure.3, UrbanGS result could be viewed as smoothing out details).\n6. **Hyperparameter sensitivity** – too many manually tuned coefficients; unclear if improvements stem from architectural changes or parameter optimization.\n\n[1] Huang, Binbin, et al. \"2d gaussian splatting for geometrically accurate radiance fields.\" *ACM SIGGRAPH 2024 conference papers*. 2024.\n\n[2] Tong, Jinguang, et al. \"GS-2DGS: Geometrically Supervised 2DGS for Reflective Object Reconstruction.\" *Proceedings of the Computer Vision and Pattern Recognition Conference*. 2025.\n\n[3] Liang, Ruofan, et al. \"Diffusion Renderer: Neural Inverse and Forward Rendering with Video Diffusion Models.\" *Proceedings of the Computer Vision and Pattern Recognition Conference*. 2025.", "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": "VZbxTfWXQc", "reviewer": "Reviewer_hz7u", "summary": "The key contribution of this paper is a more effective geometry regularization and Gaussian pruning algorithms. The proposed techniques enable a state-of-the-art performance in both the rendering quality and geometric accuracy. It also benefits the reduction of the computation burden. Extensive experiments validated the effectiveness of the method.", "questions": "See weakness.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- Originality: Simple yet effective technical innovation. \n- Quality: Solid and comprehensive experiments with subtle flaws.\n- Clarity: Fine, but there is still space for improvement.\n- Significance: A realistic yet effective large-scale scene reconstruction is an important problem. The paper effectively pushes the performance bound forward and provides a good solution.", "weaknesses": "1. There are many hyperparameters to manually tune, such as $\\alpha$, $\\beta$, and $\\gamma$ of Eq.(15), $\\gamma_d$ and $\\tau$ in Eq.(11), making the application to custom datasets miserable.\n2. There is some missing in the ablation. For SAGP ablation in Tab.3, the authors should compare with other candidates, like the one used in CityGaussianV2, instead of omitting it for comparison.\n3. There are some flaws in the presentation. The font size of Fig.5 is too small, making it hard to read. Besides, the title for the x-axis of its left part seems to be missing. The highlighted difference in Fig.3 should also be zoomed in for better details.", "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)