Related papers: GigaGS: Scaling up Planar-Based 3D Gaussians for Large Scene Surface Reconstruction

GigaGS: Scaling up Planar-Based 3D Gaussians for Large Scene Surface Reconstruction

URL: http://arxiv.org/abs/2409.06685v1
Date: Tue, 10 Sep 2024 17:51:39 GMT
Title: GigaGS: Scaling up Planar-Based 3D Gaussians for Large Scene Surface Reconstruction
Authors: Junyi Chen, Weicai Ye, Yifan Wang, Danpeng Chen, Di Huang, Wanli Ouyang, Guofeng Zhang, Yu Qiao, Tong He,
Abstract summary: 3D Gaussian Splatting (3DGS) has shown promising performance in novel view synthesis. We make the first attempt to tackle the challenging task of large-scale scene surface reconstruction. We propose GigaGS, the first work for high-quality surface reconstruction for large-scale scenes using 3DGS.
Score: 71.08607897266045
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 3D Gaussian Splatting (3DGS) has shown promising performance in novel view synthesis. Previous methods adapt it to obtaining surfaces of either individual 3D objects or within limited scenes. In this paper, we make the first attempt to tackle the challenging task of large-scale scene surface reconstruction. This task is particularly difficult due to the high GPU memory consumption, different levels of details for geometric representation, and noticeable inconsistencies in appearance. To this end, we propose GigaGS, the first work for high-quality surface reconstruction for large-scale scenes using 3DGS. GigaGS first applies a partitioning strategy based on the mutual visibility of spatial regions, which effectively grouping cameras for parallel processing. To enhance the quality of the surface, we also propose novel multi-view photometric and geometric consistency constraints based on Level-of-Detail representation. In doing so, our method can reconstruct detailed surface structures. Comprehensive experiments are conducted on various datasets. The consistent improvement demonstrates the superiority of GigaGS.

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