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 10x 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.
Quantitative Comparison with SOTA reconstruction methods. “NaN“ means no results due to NaN error, and “OOM“ denotes out-of-memory error. “FAIL“ means the method fails to extract meaningful mesh due to poor convergence. We introduce TnT-style geometry evaluation protocol for large-scale scenes. Please refer to the paper for more details.
Compared wity CityGS V1 (600M), our V2 significantly reduce the time and VRAM cost in training, while achieving comparable rendering quality (PSNR) and superior geometric accuracy (F1-Score). The tiny version (290M) is compact and efficient, making it well-suited for deployment on low-end devices like smartphones or VR headsets. Results are from Table 2 of the paper.
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