TY - GEN
T1 - An Adjustable Farthest Point Sampling Method for Approximately-sorted Point Cloud Data
AU - Li, Jingtao
AU - Zhou, Jian
AU - Xiong, Yan
AU - Chen, Xing
AU - Chakrabarti, Chaitali
N1 - Funding Information:
ACKNOWLEDGMENT This paper was supported in part by a grant from AFRL and DARPA under agreement number FA8650-18-2-7864.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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) to aggressively reduce the complexity of FPS without compromising the sampling performance. AFPS, parameterized by M, 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. On a multi-core platform, AFPS method with M = 32 can achieve 30× 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. On the ShapeNet part segmentation task, using the NPDU method on AFPS on a point cloud with 2K-32K points helps achieve a 34-280× speedup with 0.8490 instance average mIoU (mean Intersection of Union), which is only 0.0035 lower than that of the original FPS.
AB - 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) to aggressively reduce the complexity of FPS without compromising the sampling performance. AFPS, parameterized by M, 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. On a multi-core platform, AFPS method with M = 32 can achieve 30× 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. On the ShapeNet part segmentation task, using the NPDU method on AFPS on a point cloud with 2K-32K points helps achieve a 34-280× speedup with 0.8490 instance average mIoU (mean Intersection of Union), which is only 0.0035 lower than that of the original FPS.
KW - 3D Point Cloud
KW - Farthest Point Sampling
KW - LiDAR Sensor
KW - Multi-core Hardware
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U2 - 10.1109/SiPS55645.2022.9919246
DO - 10.1109/SiPS55645.2022.9919246
M3 - Conference contribution
AN - SCOPUS:85141794509
T3 - IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation
BT - 2022 IEEE Workshop on Signal Processing Systems, SiPS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 36th IEEE Workshop on Signal Processing Systems, SiPS 2022
Y2 - 2 November 2022 through 4 November 2022
ER -