上海科技大学知识管理系统

Recent years have witnessed the surge of learned representations that directly build upon point clouds. Inspired by spherical multi-view scanners, we propose a novel sampling model called Spotlights to represent a 3D shape as a compact 1D array of depth values. It simulates the configuration of cameras evenly distributed on a sphere, where each virtual camera casts light rays from its principal point to probe for possible intersections with the object surrounded by the sphere. The structured point cloud is hence given implicitly as a function of depths. We provide a detailed geometric analysis of this new sampling scheme and prove its effectiveness in the context of the point cloud completion task. Experimental results on both synthetic and real dataset demonstrate that our method achieves competitive accuracy and consistency while at a lower computational cost. The code and dataset will be released at https://github.com/goldoak/Spotlights. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.