CityGaussianV2: Efficient and Geometrically Accurate Reconstruction for Large-Scale Scenes
Authors: Yang Liu
Chuanchen Luo
Zhongkai Mao
Junran Peng
Zhaoxiang Zhang
Venue: ICLR 2025 Poster (2025)
Keywords: neural rendering
novel view synthesis
large-scale scene
radiance field
surfel splatting
surfel reconstruction
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: CityGaussianV2 addresses scalability and convergence problem of surface reconstruction algorithms, achieving SOTA geometric accuracy and high training efficiency under large scale scenes.

Abstract: Recently, 3D Gaussian Splatting (3DGS) has revolutionized radiance field reconstruction, manifesting efficient and high-fidelity novel view synthesis. However, accurately representing surfaces, especially in large and complex scenarios, remains a significant challenge due to the unstructured nature of 3DGS. In this paper, we present CityGaussianV2, a novel approach for large-scale scene reconstruction that addresses critical challenges related to geometric accuracy and efficiency. Building on the favorable generalization capabilities of 2D Gaussian Splatting (2DGS), we address its convergence and scalability issues. Specifically, we implement a decomposed-gradient-based densification and depth regression technique to eliminate blurry artifacts and accelerate convergence. To scale up, we introduce an elongation filter that mitigates Gaussian count explosion caused by 2DGS degeneration. Furthermore, we optimize the CityGaussian pipeline for parallel training, achieving up to 10$\times$ compression, at least 25\% savings in training time, and a 50\% decrease in memory usage. We also established standard geometry benchmarks under large-scale scenes. Experimental results demonstrate that our method strikes a promising balance between visual quality, geometric accuracy, as well as storage and training costs.

Published as a conference paper at ICLR 2025
CITYGAUSSIANV2: EFFICIENT AND GEOMETRICALLY ACCURATE RECONSTRUCTION FOR LARGE-SCALE SCENES
Yang Liu Chuanchen Luo', Zhongkai Mao Junran Peng & Zhaoxiang Zhang 1,2
NLPR, MAIS, Institute of Automation, Chinese Academy Sciences
2 University of Chinese Academy of Sciences
3 Shandong University 4 University of Science and Technology Beijing
{1 Liuyang2022, maozhongkai2023, zhaoxiang zhang | ac cn
chuanchen luolsdu edu. en, jxpengiever9126.c com
JK.
FSNR 10.54
PSHK 28
PSK 24 15
SSIV 0.445
FT 455
Cervorine 7K
Chas-caan 10K
O2
PI 3.49
TWTs Mrra TEA
(a) Meshing
(b) Convergence Acceleration
(c) Cost Reduction
Figure I: Illusratioon the superiority of City Gayssiaiin (a) Our method reconstructs large-scale complex scenes with accurate geometry from multi-view RGB images, restoring intricate structures of woods, buildings, and roads. (b) "Ours-coaad coarse" denotes training 2DGS with our optimization
algorithm. This strategy accelerates 2DGS reconstruction in terms of both rendering quality (PSNR, SSIM) and geometry accuracy (FI score). (c) Our optimized parallel training pipeline reduces the training time and memory by 25% and 50% respectively, while achieving better geometric quality. We report mean quality metrics in GauU-Scene (Xiong et al. 2024) here, with best performance in each column highlighted in bold.
ABSTRACT
Recently, 3D Gaussian Splatting (3DGS) has revolutionized radiance field recon- struction, manifesting efficient and high-fidelity novel view synthesis. However, accurately representing surfaces, especially in large and complex scenarios, re- mains significant challenge due to the unstructured nature of 3DGS. In this paper, we present CityGaussian V2, a novel approach for large-scale scene reconstruction that addresses critical challenges related geometric accuracy and efficiency. Building on the favorable generalization capabilities of 2D Gaussian Splatting (2DGS), we address its convergence and scalability issues. Specifically, we imple- ment a decomposed -rwgggraddiggaaaee densification and depth regression technique eliminate blurry artifacts and accelerate convergence. To scale up, we introduce elongation filter that mitigates Gaussian count explosion caused by 2DGS de- generation. Furthermore, we optimize the CityGaus pipeline for parallel train- ing, achieving up 10x compression, at least 25% savings in training time, and a 50% decrease in memory usage. We also established standard geometry bench- marks under large-scale scenes. Experimental results demonstrate that our method strikes a promising balance between visual quality, geometric accuracy, as well as storage and training costs. More live demos and official code implementation available at our project page: https::/dekuliuttssle

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Published as a conference paper at ICLR 2025
INTRODUCTION
3D scene reconstruction a long-standing topic in computer vision and graphies, with its core pur- suit of photo-realistic rendering and accurate geometry reconstruction. Beyond Neural Radiance Fields (NeRF) (Mildenhall et al.] 2021), ,. 3D Gaussian Splatting (3DGS) (Kerbl et al. 2023) has become the predominant technique in this area due to its superiority in training convergence and rendering efficiency. 3DGS represents the scene with a set of discrete Gaussian ellipsoids and ren- ders with highly optimized rasterizer. However, such primitives take an unordered structure and do not correspond well to the actual surface of the scene. This limitation impairs its synthesis quality at extrapolated views and hinders its downstream application in editing, animation, and relighting (Guédon & Lepetit 2024). Recently, many excellent works (Guédon & Lepetit 2024 Huang et al., 2024 Yu et al. 2024c) have been proposed to address this issue. Despite their great success in single objects or small scenes, devils emerge when applying them directly to complex, large-scale scenes.
On the one hand, existing methods face significant challenges related to scalability and general- ization ability. For example, SuGaR (Guédon & Lepetit| (024) binds meshes with Gaussians for refinement. However, it struggles to recover complex geometry details (Fig. [6) and can trigger out-of of-ommooooy errors when scaling up due to suboptimal implementation. GOF (Yu et al. 2024c)
struggles with large, over-blurred Gaussians. These Gaussians obstruct the field of view and hinder valid supervision, leading to severe underfitting and shell-like mesh that is non-trivial to remove, as validated in Fig. 7 and Fig. in Appendix. While 2DGS (Huang et al. 2024) exhibits better generalization ability,
shown Tab. | its convergence is hindered by the blurred Gaussians illus- trated in part (b) of Fig.
D Additionally, when scaling up through parallel training, it suffers from a Gaussian count explosion. as depicted in Fig. 3 Another challenge lies in the evaluation protocol: due to insufficient observations in boundary regions, geometry estimation becomes error-pront and unstable in these areas. As result, the metrics can significantly fluctuate and underestimate actual performance (Xiong et al. 2024). making it difficult to objectively evaluate and compare algorithms.
On the other hand, achieving efficient parallel training and compression is critical realizing geo- metrically accurate reconstruction of large-scale scenes. The total number of Gaussians can increase to 19.3 million during parallel training, resulting in a storage requirement of 4.6 GB and a memory cost of 31.5 GB, while rendering speed drops below 25 FPS. Additionally, existing VastGaussian (Lin et al., 2024 costs nearly hours for training. and CityGaussian Liu al. (0024) consumes hours to finish both training and compression. For reconstruction on low-end devices or under strict time constraints, these training costs and rendering speeds are unacceptable. Therefore, there is an urgent need for economical parallel training and compression strategy.
In response to these challenges, we introduce CityGaussian a geometrically accurate yet effi cient strategy for large-scale scene reconstruction. We take 2DGS as primitive due to its favorable generalization capabilities. To accelcrate reconstruction, we employ depth regression guided by caaampooodd-Grddintet
Densification (DGD). As shown in part (b) of Fig. 1 Tab. 2 DGD effectively eliminates blurred surfels, crucial for per- formance improvement. To address scalability, we introduce Elongation Filter mitigate the Gaussian count explosion problem associated with 2DGS degeneration during parallel training. To reduce the burden of single GPU, we conduct parallel training based on CityGaussian's block par- titioning strategy. And we streamline the process by omitting time-consuming post-pruning and distillation steps of CityGaussian. Instead, we implement spherical harmonics of degree 2 from scratch and integrate contribution-bas (c) of Fig. [ it scales up the surface quality of complex structures while significantly reducing train- ing costs. Furthermore, our contribution-based vectree quantization enables a tenfold reduction in storage requirements for large-scale 2DGS. For evaluation, we introduce TnT-style (Knapitsch et al.) 2017) protocol along with visibility -bsed crop volume estimation strategy, which can efficiently exclude underobserved regions and bring stable and consistent assessment.
Depth-Anything V2 (Yang et al., 2024) and
pruning into per-block line-tuning. As demonstrated in part
In summary, contributions are four-fold:
A novel optimization strategy for 2DGS, that accelerates its convergence under large-scale scenes and enables it to be scaled up to high capacity (Sec. 3.2)
A highly optimized parallel training pipeline that significantly reduces training costs and storage requirements while enabling real-time rendering performance (Sec. 3.3
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A TnT-style standardized evaluation protocol tailored for large, unbounded scenes, estab- lishing a geometrie benchmark for large-scale scene reconstruction (Sec.
To the best of our knowledge, our CityGaussian is among the first to implement the Gaussian radiance field in large-scale surface reconstruction. Experimental results confirm our ootte-of-the-art performance both geometric quality and efficiency.
RELATED WORKS
2.1 NOVEL VIEW SYNTHESIS
Novel view synthesis aims at generating new images from previously unseen viewpoints using im- ages captured from various source viewpoints around 3D scene. These new renderings are primar- ily based on the reconstructed 3D representation of the scene. One of the most seminal contributions to this field is Neural Radiance Fields (NeRF) (Mildenhall et al.] 2021), which implicitly models target scenes using multi-layer perceptions (MLPs). Following this, MipNeRF (Barron et al. 1021 2022] addresses objectionable aliasing artifacts by introducing anti-aliased conical frustum-baat based rendering. Deng al. 2022); Wei et al. (2021 Xu al. (2022) apply depth supervision from point cloud to accelerate model convergence. Algorithms represented by InstantNGP (Müller et al. 2022)
speeds up the training and rendering of NeRF by leveraging simplified data structures, including multi-resolution hash encoding grid and octrees (Zhang et al. [023) Wang et al.) 2022 Yu et al, 2021]. The recently emerging 3D Gaussian Splatting (Kerbl et al. (023) overcomes NeRF's draw- backs in training efficiency and rendering speed. Follow-up works further improve upon 3DGS in anti-aliasing Yu et al. (2024b), storage cost Fan et al. (2023); Zhang et al. (2024c): Navaneet et al.
2023); ; Morgenstern et al. (2023), and high-texture area underfitting Buld et al. (2024); Zhang et al.
2024b). These remarkable works have provided valuable insights into the design of our algorithm.
2.2 SURFACE RECONSTRUCTION WITH GAUSSIANS
Extracting accurate surfaces from unordered and discrete 3DGS is challenging while intriguing task. A handful of algorithms have been developed to extract unambiguous surfaces and regularize smoothness and outliers. Pioneering SuGaR (Guédon & Lepetit 2024) pretrain 3DGS and bind with extracted mesh for fine-tuning. It then relies on Poisson reconstruction algorithm for fast mesh extraction. Recent GSDF (Yu al. 2024a) and NeuSG (Chen al. 2023) optimize 3DGS together with a signed distance function to generate accurate surfaces. 2DGS (Huang et al. 2024) and concurrent GaussianSurfels (Dai al. 2024) collapse one dimension of 3D Gaussian primitives to avoid ambiguous depth estimation. The normals derived from rendering and depth map are also aligned to ensure a smooth surface. TrimGS (Fan et al. 2024) further provides a novel per-Gaussian contribution definition to remove inaccurate geometry. As post-processing technique, GS2Mesh (Wolf et al.] 2024) uses a pre-trained stereo-matching model to export mesh from 3DGS directly. GOF YYu et al.) (024c) focuses on unbounded scene. It leverages may-tracing-base volume ren- dering to obtain contiguous opacity distribution within the scene. Instead of 2DGS's TSDF-based marching-cube cube strategy, GOF gets SDF from the opacity field and marching tetrahedra to ex- tract mesh. RaDeGS (Zhang et al. 2024a) novelly define the ray intersection with Gaussian and correspondingly derive curved surface and depth distribution. Though these algorithms have been proven to be successful on small scenes or single objects, the challenges behind scaling up, includ- ing performance degradation, densification stability, and training cost, remain unexplored. We hope our analysis and design can provide more insights into the community.
2.3 LARGE-SCALL SCENE RECONSTRUCTION
Over the past few decades, 3D reconstruction from large image collections has gained considerable attention and made significant strides. Modern algorithms Tancik et al. 2022 Turki et al.) 2022] Xiangli et al., 2022 Xu et al. 2023 Zhang et al. 2023 Li al. 2024) are largely based on NeRF (Mildenhall et al. 2021). However, the substantial time required for training and rendering has hin- dered based methods for long time. The recent rise of 3DGS, exemplified by VastGaussian (Lin et al. 2024), represents paradigm shift in large-scale scene reconstruction. Subsequent devel- opments like HierarchicalGS (Kerbl et al. (204)) and OctreeGS (Ren et al., 2024) have introduced Level-of-Detail (LoD) techniques, enabling efficient rendering of scenes various scales. CityGS
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Projection
Flow
Gradicnt Flow
VC
V.C
Differentiable Rasterizer
Calculation
Coussians
Adaptive
Elongation Filter
L Alssm 1 (1 2)6 top + Exgemal
Density Control
(RCgs
Figure 2: Illustration of our optimization mechanism. We densify Gaussians exclusively according to the gradient of SSIM loss. This helps remove large and blurry Gaussians and accelerate conver- gence. Meanwhile, Gaussian count explosion shown in Fig. 3 We also supervise the rendered depth with that predicted
disable the densification of Gaussians with extreme elongation avoid the
by Depth Anything V2 Yang et 2024
This helps improve both rendering and geometry quality.
(Liu et al. al. 2024) presents comprehensive pipeline that encompasses parallel training, compression,
and LoD-based fast rendering. And DoGaussian (Chen & Lee 2024) applies Alternating Direction Methods of Multipliers (ADMM) to train 3DGS distributedly. Meanwhile, GrendelGS (Zhao al., 2024) facilitates communication between blocks on different GPUs, and FlashGS (Feng et al. 2024) significantly reduces VRAM costs for large-scale training and rendering through highly optimized renderer. Despite these advances, the issuc of geometry accuracy has been largely overlooked due to the lack of reliable benchmarks. Our work addresses this gap, proposing reliable benchmark along with a novel algorithm for both economical training, high fidelity, and accurate geometry.
3 METHOD
3.1 PRELIMINARY
3D Gaussian Splatting (Kerbl et al.
2023) represents 3D scene with a set of ellipsoids described
by 3D Gaussian distribution, i.c. GN [G, -, L,....Ni Each Gaussian contains learnable properties including central point Un E R3x1 covariance Σ, E RR333 opacity on E [0, 1]. spher- ical harmonics (SH) features fn R3x16 for view-dependent rendering. The covariance matrix further decomposed to scaling matrix S, and rotation matrix R,, i.e. En S,S,S, RR, T For certain pixel the color Cp is derived through alpha blending:
i-1
o= = Σ c,a:
II
(1-aj),
(1 (1-a)
(E>(p)
j=1
1
1 (z p1) 10
T m.)
a, o; exp
where 7 (p) denotes Gaussians located on ray crossing pixel p. and æ is the corresponding query point. The loss L that supervises 3DGS's optimization is the weighted sum of two parts, L.1 loss Li and D-SSIM loss LSSIM. 3DGS prevents under or over-reconstruction through heuristic adaptive density control, which guided by view-space position gradient, i.c. Vdensify OC/0pm. The Gaussians with gradient larger than a certain threshold would be cloned or split. For more details, we refer the readers to the original paper of 3DGS (Kerbl et al. 2023).
CityGaussian (Liu et al. 2024) aims to scale up 3DGS to large-scale scenes. As shown in Fig, first pre-trains a coarse model on full training data with the schedule of 3DGS. After that, divides Gaussian primitives and training data into non-overlapping blocks and conducts parallel tuning. Following this, it adopts the approach from LightGaussian (Fan et al., 2023), applying an additional 30,000 iterations for pruning and 10,000 iterations for distillation. Pruning removes redundant Gaussians based on their rendering importance, while the distillation reduces the spherical harmonic (SH) degree from 3 to 2. It then conducts vectree quantization for storage compression.
2D Gaussian Splatting (Huang et al, 2024) addresses surface estimation ambiguity of 3DGS by collapsing 3D ellipsoid volumes into set of 2D oriented Gaussian disks, known sur- fels. Its covariance is characterized by two tangential vectors t, and tn and a scaling vector S (Sm,a, Sn,))- In addition,2DCC incorporates depth distortion regularization and applies sur-
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Ganasion Coant Coart
Gaussian
Tae?
Floegation Filter
Elongation Filter
Seed
High-Gindient Points
Extreme- Elonmm--oongaionn Points
Bor6
7
Finctune Stop
0m
10kk 13k
20k
Image
Ground Image
Figure Illustration of the motivation and effectiveness of our Elongation Filter. We take the tuning of one block of Rubble (Turki et al. 2022) scene as example. On the left, we highlight the collection of Gaussian primitives with high gradient or extreme elongation. There a significant overlap between two collections. By restricting densification of these sand-like points, we prevent out-of-memory (OOM) errors caused by an explosion in Gaussian count, enabling a steady count evolution analogous to CityGaussian (Liu et al. 2024) in parallel tuning, as depicted the right.
face smoothness loss LNormal to align the surfel normals with those estimated from the depth map. These enhancements lead to superior results in geometry reconstruction and novel view synthesis.
3.2 OPTIMIZATION MECHANISM
This section elaborates the proposed optimization mechanism for convergence acceleration and stable training. As illustrated in Fig. 2 the mechanism comprises three components: Depth Super- vision, Elongation Filter, and Sranmpooedd-raddeet--iieeeee Densification (DGD).
As depicted in Fig. 2 2D Gaussians are projected into screen space at the given camera pose and rendered by tailored rasterizer. The derived outputs used for loss calculation. GS algorithm necessitates iterative optimization to disambiguate monocular cues from each view, ultimately con- verging coherent 3D geometry. To encourage convergence, auxiliary guidance for geometry optimization. Following the practice in Kerbl et al. (002)). we utilize Depth-Anything V2 to estimate the inverse depth and align to the dataset's 's scale, which we denote as DR- Suppose D₃ denotes the predicted inverse depth. The associated loss function defined as Depth [D - Dxl- As the training progresses, we decrease the loss weight a exponentially to suppress the adverse effect of imperfect depth estimation gradually.
incorporate depth prior as an
As discussed in Sec. [ the critical obstacle to scaling up 2DGS is the excessive proliferation of certain primitives during the parallel tuming stage. Typically, 2D Gaussian collapse to very small point when projected from a distance, especially those exhibiting extreme elongation (Huang al. 2024 With high opacity, the movement of these minuscule points cause significant pixel changes in complex scenes, leading to pronounced position gradients. As evidenced in the left portion of Fig. 3 these tiny, sand-like projected points contribute substantially to points with high gradients. And they belong to those with extreme elongation. Moreover, some points project smaller than one pixel, resulting in their covariance being replaced by fixed value through the antialiased low-pass filter. Consequently, these points cannot properly adjust their scaling and rotation with valid gradients. In block-wise parallel tuning, the views assigned to each block are much less than the total. These distant views are therefore frequently observed, causing the gradients of degenerated points accumulate rapidly. These points consequently trigger exponential increases Gaussian count and ultimately lead to oft-of-memory errors, as demonstrated in the right portion of Fig
In light of this observation, we implement a straightforward yet effective Elongation Filter to address this problem. Before densification, assess the elongation rate of each surfel, defined as 7m min($n,,u sn,v)/ max($n,u- sn,v). Surfels with 77, below a certain threshold are excluded from the cloning and splitting process. As shown in the right portion of Fig. 3 this filter mitigates out-of- memory errors and facilitates a more steady Gaussian count evolution. Furthermore, experimental results in Tab. 2 demonstrate that it does not compromise performance at the pretraining stage.
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Parallel
VecTree Quantization
Pretmin
Pruning
Distilarionn
Initial
Tuning
Compressed Scene
S1I Degnee SI1 Degres -
Parallll Tuning & Trimming
Posed Imsages
Figure 4: Illustration of pipeline modification. The pipeline of CityGS (Liu al. 2024) (dashed boxes and arrows) is compared with ours. We successfully removed time-consuming post-pruning and distillation, while enabling storage compression for 2DGS.
Naive 2DGS also suffers from suboptimal optimization when migrated to large-scale scenes. We empirically found that 2DGS is more susceptible to blurry reconstruction than 3DGS at the early training stage, as shown Fig. [0 of the Appendix. As indicated by [aang et al. (2004); Zhang al. (2024by; [Shi et al. (0024) in contrast to SSIM loss, the LI RGB loss is insensitive to blurriness and does not prioritize preserving structural integrity. Tab. of the Appendix further ablates on gradient source of adaptive density control, validating that participation of its gradient is the most critical for sub-optimal results. To alleviate this problem, we prioritize the gradient from SSIM loss and introduce Decomposed-Grradies Gradient-based Densification (DGD) strategy. Specifically, the gradient for densification is reformulated as:
V _1
densify
max
LSSIM,
LSSTM
where V LSSIM is scaled according to the average gradient norm of the total loss to align automati- cally with the original gradient threshold for densification, with w representing a constant weight.
3.3 PARALLEL TRAINING PIPELINE
As discussed in Sec. [ the post pessiinuning and distillation of CityGaussian leads to time and memory overhead. To resolve these issues, we propose novel pipeline, as shown in Fig. To bypass the distillation step, we use SH degree of 2 from the start, reducing the SH feature dimension from 48 to 27. This results considerable memory and storage savings throughout the whole pipeline. To eliminate the need for post-pruning, incorporate trimming during block-wise tuning. Specifically, we define the single-view contribution of each Gaussian following Fan et al. (2024):
(1-7)
n(p)-1
C
1
Σ (ax))
(1 (1-az) j=1
II
PR
PER,
where IP is the 2D projected region of n-th Gaussian under k-th view. n(p) denotes its depth sorted order ray crossing pixel p. ? is set as the default value of 0.5. Suppose that the images assigned to m-th block using CityGS (Liu et al. 2024)'s strategy is Vm. then the average contribution is:
C,
C,
kEV
This contribution is evaluated at the start of training and at predefined epoch intervals. Our approach differs from Fan et al. al. (0224) in that we use percentile-based threshold determine which points to discard. The points with contributions equal to or lower than this bound, including those redundant and never-observed points, will be automatically removed. Tab. 2 validates that our pipeline saves 50% storage and 40% memory, while decreasing time cost and slightly improving performance.
After merging the Gaussians across different blocks, we implement vectree quantization on 2DGS. We first evaluate each point's contribution across all training data. The least important Gaussians undergo aggressive vector quantization on the SHs. The remaining critical SHs, along with other attributes representing Gaussian shape, rotation, and opacity, stored in float 6 format.
4 GEOMETRIC EVALUATION PROTOCOLS
The evaluation protocol for rendering quality is eetl-established and transferable. We adhere to stan- dard practices by measuring SSIM, PSNR, and L.IPS between renderings and groundtruth. How-

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ToT Style Evaluation
Groundtrut
Reshape Grussians
Triining Visiility Check
Estimated Crop Volume
Evenly Sampled Point Cloud
Extracted Mesh
Point Cloud
Figure 5: Illustration of the evaluation process.
ever, there is still universally accepted protocol for assessing geometric accuracy in large-scale scene reconstruction. Recently, GauU-Scene (Xiong et al. (024) introduced the first benchmark, but its evaluation protocol overlooks boundary effects, leading unreliable assessments. For instance, as indicated in its own paper (Xiong et al.) 2024), such a protocol significantly underestimates the geometric accuracy of SuGaR, which demonstrates promising performance in mesh visualization. Moreover, GauU-Scenee does not align the surface points extraction process across methods, leading to unfair comparison. In particular, NeRF-based methods extract points from depth maps, while 3DGS utilizes Gaussian means. To address these issues, we draw lessons from the evaluation pro- tocol of the Tanks and Temple (TnT) dataset (Knapitsch al. 2017), which includes point cloud alignment, resampling, volume- bound cropping, and FI score measurement. For all the compared methods, we first extract mesh and then sample points from the surface. Though TnT's strategy of sampling vertices and face centers is fast, it would underestimate the effect of mistakenly posing large triangles. Therefore, we sample same number of points evenly on the surface.
To further deal with the challenge of boundary effect, an appropriate estimation of the crop volume necessary. The core here is to check the visible frequency of each point and estimate a bound that can exclude rarely observed points. For efficiency, we take workaround that formulates points as Gaussian primitives and checks their visibility using a well-optimized GS rasterizer. As illustrated in Fig. 5 we begin by initializing 3DGS field with the ground-trut -rrth point cloud, then traverse all training views to rasterize and count visible frequency through the output visible mask. the frequency of j-th point Ty is below a predefined threshold, it will be excluded. Then we calculate the minimum and maximum height of the remaining points, and project them to the ground plane with ground-truth transformation matrix for alpha shape estimation. Given scene covering L.47km² with 958 training views and 31.4 million ground-truth points, this process can be completed within minute if rendered in 1080p on 40G A A100. Compared the crop volume estimated all points, ours reduces the error bar length of the F1 score from 0.1 to 0.003, enabling a stable, consistent, and reliable evaluation of the model's actual performance.
Aside from automatic crop volume estimation, we also downsample the ground-trutte point cloud to accelerate the evaluation process under such large-scale scenes. The downsampling voxel size is set to 0.35m. The distance threshold of r varies from 0.3m to 0.6m, according to statistics of nearest- neighst distances in the downsampled ground-truth point clouds.
5 EXPERIMENTS
5.1 EXPERIMENTAL SETUP
Datasets. We require datasets with accurate ground-truth point clouds. Therefore, utilize the realistic dataset GauU-Scene (Xiong et al. 2024) and the synthetic dataset MatrixCity (Li et al., 2023a). From GauU-Scene, we selected the Residence, Russian Building, and Modern Building scenes. For MatrixCity, we conduct experiments on its aerial view and street view version respec- tively. Each scene comprises over 4, 4,000 training images and more than 450 test images, presenting significant challenges. These five scenes span areas ranging from 0.3 km2 to 2.7 km?. For acrial views, we follow Kerbl et al. (002)) to downsample the longer side of images to 1,600 pixels. For street views, we retain the original 1,000 1,000 resolution. To generate the initial sparse point cloud, we employ COLMAP (Schönberger & Frahm 2016 Schönberger et al. (016)) along with the provided poses. Ground-trot truth point clouds are exclusively utilized for geometry evaluation.
Implementation Details. All experiments included in this paper are conducted on 8 A100 GPUs. We set the gradient scaling factor to 0.9 and the pruning ratio 0.025. For depth distortion

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Ground-trr truth
SuGaR
2DGS
CityGS
Meden
Convergence
Failure
Figure 6: Qualitative comparison of surface reconstruction quality. Here "Russian" and "Modern" denote the Russian Building and Modern Building scene of GanU-Scene. respectively. And "Aerial" denotes aerial view of MatrixCiry. The messy results of GOF are mainly attributed to the near- ground shell-like mesh. More visualizations about GOF and street view scene are in the Appendix.
loss, we empirically find it harmful to performance, and thus set its weight to default value 0. The weight for Depth is exponentially decayed from 0.5 to 0.0025 during both the pretraining and fine- tuning stages. CNormal is activated after 7,000 iterations in pretraining and from the beginning in parallel tuning. Besides, we found that the original normal supervision was overly aggressive for complex scene reconstruction. Consequently, the weight for CNormal is reduced to 0.0125, one- fourth of its original value. We adhere to the default settings in CityGaussian (Liu et al. (024) for the learning rate and densification schedule. Due to page limitations, detailed parameters for block partition and quantization are provided the Appendix.
For depth rendering, utilize median depth for improved geometry accuracy. and for mesh extrac- tion, we employ 2DGS's bbDed algorithm with a voxel size of Im and SDF truncation of 4m. Additionally, GauU-Scene applies depth truncation of 250m, while MatrixCity uses 500m.
Baselines. We compare our method against state-of-the-arr Gaussian Splatting methods for surface reconstruction, including SuGaR (Guédon Lepetit (024). 2DGS (Huang et al. 2024), and GOF
Yu al. 2024c). Implicit NeRF-based methods such as NeuS (Wang et al. (021) and Neuralangelo (Li al. 2023b) are also included. For a fair comparison, we follow Lin al. (024); Liu et al. 2024) to double the total iterations; the starting iteration and interval of densification for bS--d or warm-up and annealing iteration of
methods are likewise doubled. We observed that GOF's mesh extraction generates an extremely high-resolution mesh exceeding 1G, significantly larger than the meshes produced by the original settings of SuGaR and 2DGS. To ensure fairness, adjusted the mesh extraction parameters of these methods to align their resolutions. For large- scale scene reconstruction, we utilize CityGaussian (Liu et al. (0224) as representative, as other concurrent aerial-view-ban based methods mesh extraction, we adopt 2DGS's methodology and use median depth for TSDF integration.
not open-sourced at the time of submission. For its
5.2 COMPARISON WITH SOTA METHODS
In this section, we compare CityGaussian with state-of-the-an (SOTA) methods both quantita- tively and qualitatively. Tab. [report results with no compression. As shown, NeRF-based methods are more prone to failure due to the NaN outputs of the MI.P or poor convergence under sparse supervision in large-scale scenes. Besides, these methods generally take over 10 hours for training- In contrast, GS-based methods finish training within several hours, while demonstrating stronger performance and generalization abilities. For GauU-Scene, our model significantly outperforms existing scometry-speciaaiiee methods rendering quality. As visually illustrated in Fig. 7 our method accurately reconstructs details such as crowded windows and woodlands. Geometrically, our model outperforms 2DGS by 0.01 Fl score. Besides, part (b) of Fig, shows that even without parallel tuning, our proposed optimization strategy enables our model to achieve significantly better
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Ground-truth
SuGaR
GDF
2065
City65
Ours
Russian
Figure 7:
Qualitative comparison of rendering quality. Here "Russian" and "Modern" denote the Russian Building and Modern Building scene of GauU-Scene, respectively. "Aerial" denotes the aerial view of MatrixCiry. The result on street view is included in Fig. of the Appendix.
Table 1: Comparison with SOTA reconstruction methods. "NaN" means no results due to NaN error. "FAIL" means the method fails to extract meaningful mesh due poor convergence, Here P and R denotes precision and recall against ground-truth point cloud, respectively.
GauU-Scene
MatrixCity-
Matrix
Methods
PSNR1
Pt
Rt
FIf
PSNR1
Pt
Rt
F1f
PSNRT
Pt
Rt
F1
NeuS
14.46
FAIL
FAIL
FAIL
16.76
FAIL
FAIL
FAIL
12.86
FAIL
FAIL
FAIL
Neuralangelo
NaN
NaN
NaN
NaN
19.22
0.080
0.083
0.081
15.48
FAIL.
FAIL.
FAIL
SuGaR
23.47
0.570
0.292
0.377
22.41
0.182
0.157
0.169
19.82
0.053
0.111
0.071
GOF
22.33
0.370
0,390
0.374
17.42
FAIL
FAIL
FAIL
20.32
0.219
0.473
0.300
2DGS
23.93
0.553
0.446
0.491
21.35
0.207
0.390
0.270
21.50
0.334
0.659
0.443
CityGS
24.75
0.522
0.405
0.453
27.46
0.362
0.637
0.462
22.98
0.283
0.689
0.401
Ours
24.51
0.576
0.450
0.501
27.23
0.441
0.752
0.556
22.24
0.376
0.759
0.503
performance in rendering and geometry at both 7K and 30K iterations, while 2DGS struggles to efficiently optimize large and blurry surfels. This validates our superiority in convergence speed. Compared to CityGS, though 0.24 PSNR is sacrificed, our method gains around 1 F1-score im- provement. As validated in Fig. 6 our meshes smoother and more complete.
On the challenging MatrixCity dataset, we evaluate performance from both aerial and street views. For
our method achieves the best surface quality among all algorithms, with the F1 score being twice that of 2DGS and outperforming CityGaussian by significant margin. Fur- thermore, GOF fails to complete training extract meaningful meshes. In the street view. CityGS and cnoometry-specalliee
methods like 2DGS significantly underperform our method geometry. As illustrated in Fig. 9 in the Appendix, our method provides qualitatively better reconstructions of road and building surfaces, with rendering quality comparable to CityGS.
Regarding training costs, indicated in Tab. the small version of CityGaussian V2 (ours-s) duces training time by 25% and memory usage by over 50%, while delivering superior geometric performance and on-par rendering quality with CityGS. The tiny version (ours-t) can even halve the training time. These advantages make our method particularly suitable for scenarios with varying quality and immediacy requirements. Results on other scenes are included in Tab, of Appendix.
5.3 ALBATION STUDIES
In this section, ablate each component of our model design. The upper part of Tab. focuses the optimization mechanism. As shown, restricting the densification of highly elongated Gaussians has negligible impact pretraining performance. However, illustrated in Fig. this strategy is

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Published as a conference paper at ICLR 2025
Table 2: Ablation model components. The experiments conducted Residence scene of GauU-Scene dataset ((Xiong et al. 2024)). Here we take 2DGS (HHuag et al. 2024)) as our base- line. The upper part ablates on pertaining, while the lower part ablates on fine-tuning. #GS, T, Size, Mem. are the number of Gaussians, total training time with 8 A100, memory, storage cost. The units are million, minute, Gigabytes, and Gigabytes respectively. The best performance of each part is in bold. "+" means add components line means that only the module in that line added, while that of other indented rows are excluded. The gray row denotes modification that is aborted and not included in the following experiments.
basis of all components in the above rows. An indented
Rendering Quality
Geometric Quality
GS Statistics
Model
SSIM
PSNR
LPIPS.
P
R1
FIf
#GS
T
Size
Mem.
FPS
Baseline
0.637
21.12
0.401
0.474
0.362
0.410
9.54
78
2.26
20.8
28.0
+ Elongation Filter
0,636
21.18
0.401
0,477
0.362
0,411
9.36
78
2.26
20.8
28.6
+ DGD
0.674
22.24
0.345
0.480
0.387
0.429
9.51
84
2.25
20.7
30.3
+ Depth Regression
0.674
22.22
0.345
0.501
0.390
0.438
9.67
89
2.29
25.3
29.4
+ Purallel Tuning
0.742
23,50
0.237
0.538
0,419
0,471
19.3
195
4.57
31.5
21.3
Trim (Ours-b)
0.742
23.57
0.243
0.534
0.430
0.477
8.07
179
1.90
19.0
31.3
Prunee
0.738
23.46
0.246
0.538
0.420
0.472
10.3
168
1.90
24.3
30.3
+ .SH Degres=2
0.742
23.49
0.245
0.540
0.423
0.474
8.06
176
1.29
14.2
34.5
+ VQQ (Ours-s)
0.740
23.46
0.248
0.530
0.414
0.465
8.06
181
0.44
14.2
34.5
pretrain (Ours-t)
0.721
23.17
0.281
0.517
0.416
0.461
5.31
115
0.29
11.5
41.7
+ partition of 2DGS
0.704
22.68
0.296
0.508
0.414
0.456
4.71
112
0.25
11.3
43.5
CityGaussian
0.727
23.17
0.266
0,519
0.402
0.453
235
0.44
31.5
66.7
essential for preventing Gaussian count explosion during the fine-tuning stage. Additionally, Tab. demonstrates that our Decomposed Densification Gradient (DGD) strategy significantly accelerates convergence, improving 1.0 PSNR, 0.04 SSIM, and almost 0.02 F1 score. A more detailed analysis of how gradient from different losses affects performance is included in the Appendix. The last two lines in the upper section confirm that depth supervision from Depth-Anything-V. (Yang et al.) 2024) enhances geometric quality considerably.
2
The lower part of Tab. 2 examines our pipeline design. With parallel tuning, both rendering and geometry quality show substantial improvements, validating the success of scaling up. For trimming. we use a more aggressive pruning ratio of 0.1, leading to 50% storage and memory reduction. The result also underscores the importance of trimming for real-time performance. LightGaussian's 's (Fan et al. 2023) pruning strategy, however, falls short in preserving rendering quality. By using an SH degree of 2 from scratch, we further reduce storage and memory usage by over 25% with marginal impact rendering performance geometry accuracy. And speed is improved by 4.2 FPS. Our contribution-ban vectree quantization step takes several minutes for compression, but achieves a 75% reduction in storage. Additionally, by using the result from 7,000 iterations as a pre-train, the total training time decreases from 3 hours to 2 hours, with the model size shrinking to below 300 MB. This compact model well-suited for deployment on low-end devices like smartphones or VR headsets. However, replacing the block partition with the one generated from 7,000 iterations of 2DGS results in considerable drop both the PSNR and score. This suboptimal outcome underscores the importance of fast convergence for efficient training of tiny models.
CONCLUSION
In this paper, we reveal the challenges scaling up the GS-based surface reconstruction method and establish the geometry benchmark for large-scale scenes. Our CityGaussianV2 takes 2DGS as primitives, eliminating its problem in convergence speed and scaling up capability. Despite that, we also implement parallel training and compression for 2DGS, realizing considerably lower training cost compared to CityGaussian Experimental results on multiple challenging datasets demonstrate the efficiency, effectiveness and robustness of our method.
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ACKNOWLEDGMENTS
This work was supported in part by the National Key R&D Program of China (No. 2022ZD01 16500), the National Natural Science Foundation of China (No. U21B2042, No. 62320106010), and part by the 2035 Innovation Program of CAS.
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A
ADDITIONAL QUALITATIVE COMPARISON
Residence
Building
Building
GoF
Qurs
Figure 8: Qualitative comparison meshes generated from GOF and our CitySaussian V2.
Ground truth
SuGaR
GCF
2DGS
Ctygs
Dur
Mest
Figure 9: Qualitative comparison of results on the street view of MatrixCity.
Grounn-- d--trtht
Flattened 3DGS
2DGS
View
Figure 10: Qualitative ablation of 7K iteration results among different methods.
This section provides additional qualitative comparisons. As illustrated in Fig, 8 the mesh produced by GOF is obscured by near-ground shell, which obstructs rendering from the test view in Fig. of the main paper and is challenging to remove, However, does successfully capture the intricate structures of buildings and landscapes. Fig. provides more thorough comparison. Notably,
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Broken Road Surface
Broken Building Facade
Inaccurate Spire
Figure 11: Bad cases visualization. Due occlusion and lack of observation, some road surfaces and building facades are not well reconstructed. TSDF-based fusion also struggles to recover some thin structures like spires.
Table 3: Detailed comparison with SOTA on rendering metrics. "NaN" here means no results due NaN error. "FAIL" means fail extract meaningful mesh due to poor convergence.
GauU-Scenc
MatriCiity
Matrix MatrixCity Street
Methods
SSIM
PSNR
LPIPS
SSIM
PSNR
LPIPS
Flf
SSIM
PSNR
LPIPS..
FIf
NeuS
0.227
14.46
0.688
FAIL
0.476
16.76
0.691
FAIL
0.562
12.86
0.514
FAIL
Neuralangelo
NaN
NaN
NaN
0.535
19.22
0.594
0.081
0.592
15.48
0.547
FAIL
SuGaR
23.47
0.633
22.41
0.493
0.169
0.662
19.82
0.071
GOF
0.705
22.33
0.374
17.42
0.588
FAIL
0.703
20.32
0,440
0,300
2DGS
0.756
23.93
0.232
0.491
0.632
21.35
0.562
0.270
0.723
21.50
0.477
0.441
CityGS
0.789
24.75
0.176
0.449
0.865
27.46
0.204
0.462
0.808
22.98
0.301
0.401
Ours
0.765
24.51
0.215
0.501
0.857
27.23
0.169
0.531
0.788
22.24
0.347
0.524
our CityGausiinn2 showcases qualitatively better reconstructions with more geometry details and fewer outliers.
Fig. visualizes the rendering and extracted mesh of state-of-the-art methods in street view. Our method successfully scales up and the rendering quality is on par with CityGaussian. In terms of geometry, as shown in the last two rows of Fig. [9] the mesh produced by SuGaR appears messy, while GOF is obscured by near-ground shells and rendered in darkness. The road reconstructed by 2DGS fragmented, and CityGaussian suffers from floating artifacts in the sky. contrast, our CityGaussian V2 achieves superior quality, constructing smoother and more complete surface for buildings and roads.
Fig. 10 compares the results at 7,000 iterations across different methods. As shown, 2DGS experi- ences more severe blurring compared to 3DGS, which significantly hampers its convergence speed. The flattened 3DGS, which modifies 3DGS by constraining one of its scalings
minimal value as done in Fan et al. (2024), introduces similar blurring effects observed in 2DGS. This suggests that the issue may be inherent the dimension collapse. In contrast, sensitivity of SSIM loss to blurriness, climinating blurry surfels while enabling much higher quality results at the same 7K iterations.
DGD strategy leverages the
B ADDITIONAL QUANTITATIVE RESULTS
In this section, we present additional quantitative results. Tab, highlights comprehensive compar- ison with state-of-the-ar (SOTA) methods in rendering metrics. Notably, our approach significantly outperforms geometry-specite methods, while maintaining comparable photometric quality with CityGS. As shown in Fig. 7 of the main paper, our model can achieve qualitatively better recon- structions with more appearance detail.
In addition to the full experimental results Tab. and Tab. 3 we provide a comparison of parallel training methods with compressed and aligned Gaussian counts in Tab. 4 For reference, include results where 2DGS directly paired with CityGS's S parallel training strategy. However, as shown, this approach encounters OOM emorr in most scenes due excessive memory demands caused
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Table 4: Detailed comparison among SOTA among parallel training methods. 2DGS* here means
applying CityGS's training strategy to 2DGS without our proposed optimization mechanism. And
"OOM" means one or more sub-blocks fail to finish training due the out-of-memory error. The best result for specific metrics under each scene is highlighted in bold.
Scene
Method
PSNRT
FI1
#GS(M)
T(min))
Size(G)
Mem.(G))
FPSt
2DGS
OOM
0OM
OOM
OOM
OOM
OOM
0OM
Residence
CityGS
23.17
0.453
8.05
235
0.44
31.5
66.7
Ours
23.46
0.465
8.07
0.44
14.2
45.5
2DGS
OOM
OOM
OOM
OOM
OOM
OOM
0OM
Russia
CitGG
24.19
0.455
7.00
209
0.38
27.4
55.2
Ours
23.89
0.537
6.97
177
0.38
33.3
2DGS OOM
OOM
OOM
OOM
OOM
OOM 29.2
OOM
Modern
CitGG
26,22
0.462
7.90
215
0.43
$7.1
Ours
25.53
0.489
7.90
185
0.42
34.5
2DGS
OOM
OOM
OOM
OOM
OOM
OOM
OOM
Aerial
CitYGS
27.23
0.459
10.3
217
0.56
25.7
38.6
Ours
0.492
10.4
0.56
2DGS
22.24
0.371
9.20
170
2.17
15.5
28.6
Street
CityGS
21.12
7.63
0.42
11.9
Ours
0.499
7.59
0.42
Table 5: Detailed geometry metrics on GauU-Scene datasets ((Xiong et al. 2024)). means that the method fails to finish 60,000 iterations training and therefore reports that of 30.000 iterations. "NaN" here means no results due to NaN error, and "FAIL" means fail to extract meaningful mesh.
Residence
Russian Building
Modern Building
Methods
Pf
R
FI
Pr
Rt
FIf
P
R1
FI1
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
2DGS
0.526
0.406
0.458
0.544
0.519
0.531
0.588
0.413
0.485
CityGS
0.524
0.448
0.459
0.443
0.451
0.582
0.381
0.461
Ours
0.524
0.421
0.467
0.560
0.530
0.544
0.643
0.398
0.492
by redundant Gaussians and the Gaussian count explosion issue illustrated in Fig. Compared to CityGS, our method (the small version, i.e. ours-s in Tab. 2 achieves superior geometric accuracy while significantly reducing training time and memory usage. Under extreme compression (e.g., 75% on Residence) or in street-view scenes, our method also delivers significantly better rendering quality. These results not only highlight the necessity of our proposed optimization strategy but also demonstrate our method's 's clear advantages over CityGS.
Tab. 5 Tab, Tab. 6]reports detailed performance GauU-Scene dataset. Comparing the quality the extracted mesh, SuGaR (Guédon & Lepetii 2024) shows promising precision on the Residence and Modern Building scene, but the overall performance is severely deteriorated by insufficient recall. And GOF Yu et al., 2024c) fails to finish 60,000 training on the Russian Building scene due to OOM error. 2DGS (Huang et al. 2024) shows competitive geometric performance, substantially outperforming CityGS. However, Tab. showcases that the geometry-specift methods fall short in rendering quality. In contrast, our method not only achieves SOTA surface quality, but also strikes promising balance with rendering fidelity.
In Tab. 7 we check the influence of different losses densification. On the one hand, Tab, shows that the most critical gradient for densification is that from LI RGB loss. Its participation has negative impact on reconstructing appearance details (SSIM) and overall quality (PSNR). On the other hand, the influence of densification gradient from normal and depth is within the error bar (0.003 for the Fl score). Therefore, we exclusively rely on the gradient from SSIM loss in the official version of our CityGaussian
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Table 6: Detailed rendering metrics on Gaul-Scenee datasets ((iongg al., 2024).
means that
the method fails to finish 60,000 iterations training and therefore reports that of 30,000 iterations. "NaN" here means no results due NaN error.
Residence
Russian Building
Modern Building
Methods
SSIM
PSNR
LPIPS
SSIM
PSNR
LPIPS]
SSIM1
PSNR
LPIPS
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
2DGS
0.703
22.24
0.306
0.788
23.77
0.189
0.776
25.77
0.202
CityGS
0.763
23,59
0.204
0.808
24.37
0.163
0.796
26.29
0.160
Ours
0.742
23.57
0.243
0.784
24.12
0.196
0.770
25.84
0.207
Table 7: Ablation on gradient source of densification. The experiments are conducted on the Resi- dence scene of the GauU-Scene dataset ((Xiong et al., 2024)). Here we take 2DGS ((Huang al., 2024)) with the Elongation Filter as the baseline. #GS and TT are the number of Gaussians and total training time with 8 A100 respectively. The best performance of each metric column is highlighted in bold. Notably, though the densification gradients here are not automatically scaled, the numbers of Gaussians are maintained at similar levels.
Densification Gradient
Rendering Quality
Geometric Quality
GS Statisties
SSIM
RGB
NORM DEPTH
PSNR
SSIM
LPIPS
R
#GS(M)
T(min)
n/a
0.636
21.18
0.401
0.464
0.353
0.401
9.56
78
n/a
0.635
21.13
0.403
0.463
0.350
0.399
9.54
85
n/a 0.673
22.21
0.347
0.466
0.377
0.417
9.44
85
n/a
0.674
22.24
0.345
0.470
0.378
0.419
9.51
84
0.674
22.22
0.345
0.490
0.381
0.429
9.67
89
0.674
22.21
0.347
0.490
0.380
0..428
9.43
90
C MORE IMPLEMENTATION DETAILS
For primitives and data partitioning, as well as parallel tuning, we follow the default parameter setting of CatsSans (Liu et al.) 2024) on both aerial view and street view of MatrixCity dataset. To be specific, it applies lower learning rates during tuning compared to pertaining, and the street view is trained with a significantly lower learning rate and longer densification interval due to its extreme view sparsity (Zhou et al. 2024). On GanU-Scene, we use SSIM threshold of 0.05 and default foreground range for contraction. i.c. the central 1/3 area of the scene. The Residence scene of GauU-Scene is divided into x 2 blocks, while Russian Building and Modern Building scenes divided into x 3 blocks. When fine-tuning on GauU-Scene the learning rate of position is reduced by 60%, while that of scaling is empirically reduced by 20%, as suggested in Liu al. 2024). For vectree quantization, we set the codebook size to 8192 and the quantization ratio to 0.4.
D DISCUSSION
While our method successfully delivers favorable efficiency and accurate geometry reconstruction for large-scale scenes, we also want to discuss its limitations: Firstly, this paper evaluates on the GauUScene and MatrixCity, which feature compensated or idcally constant lighting conditions. Nevertheless, we trust that the consideration of illumination variance and incorporating techniques like decoupled appearance modeling would be helpful for the model's adaptability. Secondly, for mesh extraction, occlusion and lack of observation hinder reconstruction of some road surfaces and building facades. Additionally, TSDF fusion struggles with thin structures, such as spires shown in Fig. II Applying more efficient training strategies and advanced mesh extraction algorithms could address these issues. Thirdly, although compression strategy significantly enhances rendering speed, it still lags behind CityGS even when sharing similar Gaussian counts. Extensive experiments in Tab. 4 validate this conclusion. Future work should explore deeper optimizations of rasterizers,
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such those proposed by Feng et al. (2024), the integration of Level of Detail (LoD) techniques Kerbl et al. (2024); Ren et al. (2024).
18


Reviews:
[{"review_id": "sQ3sUNNIXB", "reviewer": "Reviewer_LweB", "summary": "This paper presents CityGaussianV2, a new method for large-scale 3D scene reconstruction that builds on a variant of Gaussian Splatting, 2D Gaussian Splatting.\nCityGaussianV2 introduces 3 contributions to resolve scaling problem in large scale scene reconstruction. 1) Depth supervision which uses pre-trained depth estimator to supervise inverse depth, 2) decomposed-gradient-based densification (DGD) which adaptively densifies based on SSIM, and 3) elongation filter to accelerate convergence and mitigate the Gaussian count explosion problem. \nIn addition to the core technical contributions, the paper presents an optimized parallel training pipeline and new evaluation protocol for large-scale scenes that addresses the limitations of existing benchmarks by accounting for boundary effects.", "questions": "How were the baseline hyper-parameters tuned? It seems to me that the OOM and convergence may be overcome by the best set of hyperparameters in few of the existing models. \n\nHow did the authors design the new techniques? I’m curious as to what led to the decisions of such new optimization techniques that are helpful, instead of other potential choices that may be similar in results.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The paper solves one of the tougher remaining challenges in 3D reconstruction with Gaussian Splatting: large scale scene reconstruction. The qualitative and quantitative results of the paper are outstanding. Compared to existing baselines, the paper demonstrates faster convergence, more accurate photo-realistic reconstruction and lower memory usage. \nMoreover, the reviewer appreciates additional contributions such as new evaluation criteria that better captures geometric details of larger scenes. The paper spends a lot of effort on being fair on evaluation. The method compares with all SotA methods that the reviewer is aware of.", "weaknesses": "The technical novelty of the paper seems rather weak. The paper is built on top of existing works: 2D GS, Depth anything. While the paper has demonstrated a good way to fuse these existing works in a nice scalable manner, the provided contributions seem like multiple ad-hoc solutions to circumvent an underlying core problem: solving densification in a scalable way. \n\nIn other words, the reviewer expects solid theoretical reasoning on why their optimization techniques are justified, instead of relying on empirical demonstration of good qualitative results. Otherwise, it is hard to justify whether the contributions of the work provided by authors are coming from meta-fine tuning of the existing 3D reconstruction dataset, or whether they are actual improvements.", "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": "3XRARI5Jt4", "reviewer": "Reviewer_tjuJ", "summary": "This paper presents CityGaussianV2, a novel methodology for 3D scene reconstruction tailored for large-scale urban environments. It builds on the 2D Gaussian Splatting technique by incorporating advanced features such as Decomposed-Gradient-based Densification, depth supervision, and an Elongation Filter. These enhancements collectively aim to improve geometric accuracy and reduce computational demands.", "questions": "Please see the weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The manuscript is well-constructed, with a logical flow that enhances readability. \n\nThe use of technical terminology is appropriate, and complex concepts are elucidated with commendable clarity. The integration of innovative techniques like DGD and the Elongation Filter is particularly noteworthy, as these elements not only speed up training convergence but also refine rendering quality, effectively mitigating the Gaussian number explosion issue often seen in 3D Gaussian Splatting of large-scale scenes.", "weaknesses": "1. The manuscript explores outdoor reconstruction, yet it does not fully address the incorporation of techniques for improving robustness against variable lighting conditions and viewing angles. Techniques similar to the decoupled appearance models used in GOF could potentially bolster the system's adaptability.\n\n2. The proposed method employs an Elongation Filter to prevent excessive Gaussian numbers, but it lacks a mechanism for handling cases where splitting is necessary. Will directly limiting the Gaussian split result in some areas being under reconstruction? At the same time, why does the number of Gaussians in the method plus an Elongation Filter in Table 2 exceed the baseline? Shouldn't the filters be limited to the number of Gaussian functions?\n\n3. There appears to be a discrepancy between the Gaussian count reported in Figure 3 and the data presented in Table 2. The number of CityGaussian is less than that of the author's method (w Elongation Filter), it is inconsistent with the results shown in Table 2. \n\n4. The limitations are only briefly touched upon in the appendix. A detailed examination within the main body of the paper would provide a more balanced perspective and foster a clearer understanding of the method's scope and potential constraints.\n\n5. The metrics presented in Table 2 (e.g., number of Gaussians, processing time, memory usage) should specify the units of measurement to aid in interpretation.", "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": "ZyzhFRKT8w", "reviewer": "Reviewer_M9Gm", "summary": "The authors propose an optimization framework for Gaussian splitting, to address scalability issues when reconstructing large-scale scenes. The proposed framework builds on top of CityGaussian and is named CityGaussianV2 but addresses convergence and scalability issues. They propose the use of DGB densification and depth regression to improve blurry artifacts and accelerate convergence, an elongation filter to mitigate the Gaussian count explosion in 2DGS and a parallel training technique to improve training times and memory consumption.", "questions": "See above", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "The authors address critical issues in adapting Gaussian splitting techniques in large-scale 3D reconstructions. The proposed framework addressed issues related to scalability as well as reducing the costs associated with training large-scale Gaussian splitting models. The results of the proposed model show that the method is able to scale up CityGaussian without degrading visual quality and geometric accuracy. The paper is well-written and the ideas are well explained. The experimental evaluation is complete and supports the claims in the paper. The authors also provide ablation studies to support their design choices.", "weaknesses": "Minor weaknesses overall \n1) The novelty of the paper is limited but addresses a very important aspect of large-scale 3D reconstruction using gaussian splitting.\n\n2) Lines 189-190 It looks like R and S symbols are flipped assuming that R is rotation and S is scaling then the decomposition of the covariance matrix makes sense. \n\n3) Line 352-352 How do you estimate the ground plane? Could you provide some more details about the number of points, the scale of the scene, and the hardware used so that this process can be completed in 1 minute?", "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": "xcOvEdidxy", "reviewer": "Reviewer_k1eM", "summary": "The paper proposes a large-scale scene geometry reconstruction method based on Gaussian splatting. Specifically, it implements parallel training and compression for 2D Gaussian splatting, incorporates regression based on Depth Anything V2, and designs an Elongation Filter along with a Decomposed-Gradient-based Densification. Additionally, the paper establishes a benchmark for large-scale scene geometry reconstruction for evaluation purposes. The paper demonstrates through comparison experiments that its method achieves superior reconstruction quality on large-scale scenes.", "questions": "Apart from the weaknesses mentioned, there are some additional questions:\n1. When running the baseline models, was the same hardware used (8 A100 GPUs)? This information should be included in the paper.\n2. While the reconstruction quality surpasses CityGS, the rendering quality is slightly inferior across all datasets. What could be the potential reason for this?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper is clearly written.\n2. It implements a comprehensive process for large-scale surface reconstruction, addressing issues in previous methods with thoughtful design, offering a substantial engineering contribution.\n3. The paper conducts thorough ablation studies, analyzing the impact of each module on rendering, reconstruction quality, training time, storage requirements, and other factors.\n4. The establishment of a large-scale geometry reconstruction benchmark is also a significant and meaningful contribution.", "weaknesses": "1. The method comprises multiple combined modules, some of which are known techniques or modified versions of known techniques, so overall, the novelty is somewhat limited. Moreover, the ablation results suggest that some modules have only minor impacts.\n2. In the comparison experiments, the authors should also include metrics such as the number of Gaussians, training time, and memory usage for each method, in addition to rendering and reconstruction quality. For certain baseline methods like 2DGS, adjusting hyperparameters can increase the number of Gaussians to enhance the results. The authors should demonstrate that their method has advantages in rendering and reconstruction quality compared to baselines under similar training times and storage usage; otherwise, the results are not fully convincing.", "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)