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.

翻译：近年来，基于点云直接构建的学得表示呈现井喷式发展。尽管这些表示的表达能力日益增强，但现有的大多数表示方法仍难以生成有序的点集。受球形多视角扫描仪的启发，我们提出一种名为Spotlights的新型采样模型，将三维形状表示为紧凑的一维深度值数组。该模型模拟均匀分布在球面上的相机配置，每个虚拟相机从其主点通过同心小球形帽上的样本点投射光线，以探测与球面包围物体可能存在的交点。由此，结构化点云被隐式定义为深度的函数。我们对该新型采样方案进行了详细的几何分析，并在点云补全任务中验证其有效性。在合成数据和真实数据上的实验结果表明，我们的方法在显著降低计算成本的同时，实现了具有竞争力的精度与一致性。此外，在下游的点云配准任务中，我们展现了相较于现有最优补全方法的优越性能。