CityGaussianV2：大规模场景的高效几何精确重建

CityGaussianV2: Efficient and Geometrically Accurate Reconstruction for Large-Scale Scenes

November 1, 2024

作者: Yang Liu, Chuanchen Luo, Zhongkai Mao, Junran Peng, Zhaoxiang Zhang

cs.AI

摘要

最近，3D高斯飞溅（3DGS）已经彻底改变了辐射场重建，展现出高效和高保真度的新视角合成。然而，在大型和复杂场景中准确表示表面，仍然是一个重要挑战，这是由于3DGS的非结构化特性所致。本文提出了CityGaussianV2，这是一种针对大规模场景重建的新方法，解决了与几何精度和效率相关的关键挑战。借鉴2D高斯飞溅（2DGS）良好的泛化能力，我们解决了其收敛性和可扩展性问题。具体而言，我们实现了一种基于分解梯度的致密化和深度回归技术，以消除模糊伪影并加快收敛速度。为了扩展规模，我们引入了一种延伸滤波器，以减轻由2DGS退化引起的高斯计数爆炸。此外，我们针对并行训练优化了CityGaussian管道，实现了高达10倍的压缩，至少节省了25%的训练时间，以及50%的内存使用减少。我们还在大规模场景下建立了标准几何基准。实验结果表明，我们的方法在视觉质量、几何精度以及存储和训练成本之间取得了有希望的平衡。项目页面位于https://dekuliutesla.github.io/CityGaussianV2/。

English

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/.