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面临的挑战，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单次点云简化方法，无需任何先验表面重建步骤即可保留点云的显著结构特征与整体形状。该方法采用适用于黎曼流形上函数的自注意高斯过程，对任意点云表面的变化函数进行建模。通过贪婪稀疏化方案逐点选择，得到原始点云的简化版本。该方案的选择准则确保简化后的点云最能表征原始点云的表面变化特征。我们在多个基准数据集及自采集点云上评估本方法，与现有多种方法进行对比，并展示了其在下游配准与表面重建任务中的应用效果。实验表明，本方法在经验性能与计算效率方面均具有竞争力。