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 with kernels 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 datasets, compare it to a range of existing methods and show that our method is competitive both in terms of empirical performance and computational efficiency.
翻译:处理、存储和传输大规模点云是计算机视觉领域持续面临的挑战,这也阻碍了三维模型在自动驾驶、虚拟现实和遥感等实际应用中的进展。我们提出了一种新颖的、一次性点云简化方法,它可以保留重要的结构特征和整个点云的形状,在不需要先进行表面重构的情况下完成。我们的方法采用定义在黎曼流形上的高斯过程内核,可以对任何给定的点云模型表面变化函数建模。通过使用贪心的分布式选点策略,我们可以顺序地选取点,从而获得原始点云的简化版本。选择标准确保简化云最好地表示原始点云的表面变化。我们在几个基准数据集上评估我们的方法,并将其与现有方法进行比较,结果表明我们的方法在实证性能和计算效率方面具有竞争力。