The processing, storage and transmission of large-scale point clouds is an ongoing challenge in the computer vision community which hinders progress in the application of 3D models to real-world settings, such as autonomous driving, virtual reality and remote sensing. We propose a novel, one-shot point cloud simplification method which preserves both the salient structural features and the overall shape of a point cloud without any prior surface reconstruction step. Our method employs Gaussian processes suitable for functions defined on Riemannian manifolds, allowing us to model the surface variation function across any given point cloud. A simplified version of the original cloud is obtained by sequentially selecting points using a greedy sparsification scheme. The selection criterion used for this scheme ensures that the simplified cloud best represents the surface variation of the original point cloud. We evaluate our method on several benchmark and self-acquired point clouds, compare it to a range of existing methods, demonstrate its application in downstream tasks of registration and surface reconstruction, and show that our method is competitive both in terms of empirical performance and computational efficiency.

翻译：大规模点云的处理、存储与传输是计算机视觉领域持续存在的挑战，阻碍了三维模型在自动驾驶、虚拟现实及遥感等真实场景中的应用。我们提出一种新颖的一次性点云简化方法，无需任何前置曲面重建步骤，即可在保留点云显著结构特征的同时保持其整体形状。该方法采用适用于黎曼流形函数的高斯过程，可对任意点云的表面变化函数进行建模。通过贪婪稀疏化方案依次选取点，可获得原始点云的简化版本。该方案的选点准则确保简化点云最优地表征原始点云的表面变化。我们在多个基准数据集及自采点云上评估所提方法，与现有多种方法进行对比，展示其在配准与曲面重建下游任务中的应用，并证明本方法在经验性能与计算效率方面均具竞争力。