CityGaussianV2: Efficient and Geometrically Accurate Reconstruction for
  Large-Scale Scenes
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/.
6728e5426cb07b4d13e84fe8
Yang Liu
claimed_verified
2024-11-05T07:58:36.004Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/rJIMhaB4NB8bvBtmsw1vA.png
Yang Liu
user
TeslaYang123
6728e5426cb07b4d13e84fe9
Chuanchen Luo
admin_assigned
2024-11-04T21:27:19.763Z
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/avatars/4e4a489771bcf5fbef1530e179e2e0c5.svg
Chuanchen Luo
user
LogicTrainer
6728e5426cb07b4d13e84fea
Zhongkai Mao
6728e5426cb07b4d13e84feb
Junran Peng
admin_assigned
2024-11-04T21:27:44.185Z
62c11a7360edb2dd7765b80d
/avatars/92db3258d0ba44daee22952f0644cd93.svg
Junran Peng
user
JrPeng
6728e5426cb07b4d13e84fec
Zhaoxiang Zhang
admin_assigned
2024-11-04T21:27:38.228Z
6350d59989def14ad21e11b3
/avatars/9a7ae6131e0a89f3461f267ee844b218.svg
z
user
smallbert
CityGaussianV2: Efficient and Geometrically Accurate Reconstruction for
  Large-Scale Scenes
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/.
CityGaussianV2 improves large-scale scene reconstruction by enhancing geometric accuracy and efficiency through decomposed-gradient-based densification, depth regression, and parallel training optimizations.
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