论文标题
可调节的最远点采样方法，用于近似分数云数据
An Adjustable Farthest Point Sampling Method for Approximately-sorted Point Cloud Data
论文作者
论文摘要
采样是原始点云数据处理的重要组成部分，例如在流行的PointNet ++方案中。最流行的采样方案之一是最流行的抽样方案之一，最远的点采样且执行距离更新，最远的点采样（FPS）是最受欢迎的点采样（FPS）。不幸的是，它的效率低，并且可能成为点云应用的瓶颈。我们提出了由M参数化的可调节FPS（AFP），以积极地降低FPS的复杂性，而不会损害采样性能。具体而言，它将原始点云分为M小点云，并同时将样品M点分为M点。它利用了大约分类点云数据的尺寸局部性，以最大程度地减少其性能降解。 AFPS方法可以在原始FPS上实现22至30倍的速度。此外，我们提出了最近的点距离上升（NPDU）方法，以将距离更新数限制为恒定数字。 AFPS方法上的NPDU组合可以在具有2K-32K点的点云上实现34-280倍的加速，其算法性能与原始FPS相当。例如，对于Shapenet部件分割任务，它实现了0.8490实例的平均MIOU（联合平均值），与原始FPS相比，它仅下降0.0035。
Sampling is an essential part of raw point cloud data processing such as in the popular PointNet++ scheme. Farthest Point Sampling (FPS), which iteratively samples the farthest point and performs distance updating, is one of the most popular sampling schemes. Unfortunately it suffers from low efficiency and can become the bottleneck of point cloud applications. We propose adjustable FPS (AFPS), parameterized by M, to aggressively reduce the complexity of FPS without compromising on the sampling performance. Specifically, it divides the original point cloud into M small point clouds and samples M points simultaneously. It exploits the dimensional locality of an approximately sorted point cloud data to minimize its performance degradation. AFPS method can achieve 22 to 30x speedup over original FPS. Furthermore, we propose the nearest-point-distance-updating (NPDU) method to limit the number of distance updates to a constant number. The combined NPDU on AFPS method can achieve a 34-280x speedup on a point cloud with 2K-32K points with algorithmic performance that is comparable to the original FPS. For instance, for the ShapeNet part segmentation task, it achieves 0.8490 instance average mIoU (mean Intersection of Union), which is only 0.0035 drop compared to the original FPS.
