Spotlights: Probing Shapes from Spherical Viewpoints

TL;DR

Spotlights introduces a novel spherical sampling model for 3D shape representation that efficiently captures point cloud data, improving accuracy and computational efficiency in shape completion and registration tasks.

Contribution

The paper proposes a new spherical sampling scheme called Spotlights, enabling implicit point cloud representation with reduced computational cost and enhanced performance in shape completion and registration.

Findings

Achieves competitive accuracy in point cloud completion

Reduces computational cost significantly

Outperforms state-of-the-art methods in registration tasks

Abstract

Recent years have witnessed the surge of learned representations that directly build upon point clouds. Though becoming increasingly expressive, most existing representations still struggle to generate ordered point sets. 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 through sample points on a small concentric spherical cap to probe for the 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 data demonstrate that our method achieves competitive accuracy and consistency while having a significantly reduced computational cost. Furthermore, we show superior performance on the downstream point cloud registration task over state-of-the-art completion methods.