title: CityGaussianV2: Efficient and Geometrically Accurate Reconstruction for
    Large-Scale Scenes
summary: 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
  10times 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. The project page is available at
  https://dekuliutesla.github.io/CityGaussianV2/.
paper_authors._id: 6728e5426cb07b4d13e84fe8
paper_authors.name: Yang Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-05T07:58:36.004Z
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paper_authors.fullname: Yang Liu
paper_authors.type: user
paper_authors.user: TeslaYang123
paper_authors._id: 6728e5426cb07b4d13e84fe9
paper_authors.name: Chuanchen Luo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-04T21:27:19.763Z
paper_authors._id: 6423d96030b0e4ab36dcd5b7
paper_authors.avatarUrl: /avatars/4e4a489771bcf5fbef1530e179e2e0c5.svg
paper_authors.fullname: Chuanchen Luo
paper_authors.type: user
paper_authors.user: LogicTrainer
paper_authors._id: 6728e5426cb07b4d13e84fea
paper_authors.name: Zhongkai Mao
paper_authors._id: 6728e5426cb07b4d13e84feb
paper_authors.name: Junran Peng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-04T21:27:44.185Z
paper_authors._id: 62c11a7360edb2dd7765b80d
paper_authors.avatarUrl: /avatars/92db3258d0ba44daee22952f0644cd93.svg
paper_authors.fullname: Junran Peng
paper_authors.type: user
paper_authors.user: JrPeng
paper_authors._id: 6728e5426cb07b4d13e84fec
paper_authors.name: Zhaoxiang Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-04T21:27:38.228Z
paper_authors._id: 6350d59989def14ad21e11b3
paper_authors.avatarUrl: /avatars/9a7ae6131e0a89f3461f267ee844b218.svg
paper_authors.fullname: z
paper_authors.type: user
paper_authors.user: smallbert
paper_title: CityGaussianV2: Efficient and Geometrically Accurate Reconstruction for
    Large-Scale Scenes
paper_summary: 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
  10times 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. The project page is available at
  https://dekuliutesla.github.io/CityGaussianV2/.
paper_ai_summary: CityGaussianV2 improves large-scale scene reconstruction by enhancing geometric accuracy and efficiency through decomposed-gradient-based densification, depth regression, and parallel training optimizations.
paper_ai_keywords: 3D Gaussian Splatting radiance field reconstruction novel view synthesis 2D Gaussian Splatting geometric accuracy decomposed-gradient-based densification depth regression Gaussian count explosion parallel training benchmarking