arXiv 2409.085622024-09-13cs.CV

CSS：克服众包3D高斯泼溅中的位姿与场景挑战

CSS: Overcoming Pose and Scene Challenges in Crowd-Sourced 3D Gaussian Splatting

Beijing University of Posts and Telecommunications（北京邮电大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；China Unicom Smart City Research Institute（中国联通智慧城市研究院）

Runze Chen, Mingyu Xiao, Haiyong Luo, Fang Zhao, Fan Wu, Hao Xiong, Qi Liu, Meng Song

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AI总结：

本研究提出新型3D高斯泼溅管线CSS，借助鲁棒几何先验与先进光照建模，解决众包图像无位姿场景重建中位姿缺失、视角有限、光照不均的难题，实现复杂真实场景下的高质量新视角合成，性能优于现有方法，可支撑AR、VR及大规模3D重建应用。

摘要：

我们提出了众包泼溅（Crowd-Sourced Splatting，CSS）——一种新型3D高斯泼溅（3D Gaussian Splatting，3DGS）管线，旨在解决利用众包图像进行无位姿场景重建的难题。从照片集合中重建具有历史意义却难以抵达的场景，这一愿景长期吸引着研究人员。但传统3D技术难以应对相机位姿缺失、视角有限以及光照不一致的问题。CSS通过鲁棒的几何先验和先进的光照建模解决这些挑战，能在复杂真实场景下实现高质量新视角合成。我们的方法相较现有方案实现了显著提升，为AR、VR和大规模3D重建中更精准灵活的应用铺平了道路。

英文摘要：

We introduce Crowd-Sourced Splatting (CSS), a novel 3D Gaussian Splatting (3DGS) pipeline designed to overcome the challenges of pose-free scene reconstruction using crowd-sourced imagery. The dream of reconstructing historically significant but inaccessible scenes from collections of photographs has long captivated researchers. However, traditional 3D techniques struggle with missing camera poses, limited viewpoints, and inconsistent lighting. CSS addresses these challenges through robust geometric priors and advanced illumination modeling, enabling high-quality novel view synthesis under complex, real-world conditions. Our method demonstrates clear improvements over existing approaches, paving the way for more accurate and flexible applications in AR, VR, and large-scale 3D reconstruction.

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