CityGaussianV2: Efficient and Geometrically Accurate Reconstruction for
  Large-Scale Scenes
Authors: name: Yang Liu, name: Chuanchen Luo, name: Zhongkai Mao, name: Junran Peng, name: Zhaoxiang Zhang

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/.