An Adjustable Farthest Point Sampling Method for Approximately-sorted Point Cloud Data-Reference-Cited by-同舟云学术

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An Adjustable Farthest Point Sampling Method for Approximately-sorted Point Cloud Data

Published:2022-11-02 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Workshop on Signal Processing Systems (SiPS)
language:
Short-container-title:

Author:

Li Jingtao¹,Zhou Jian²,Xiong Yan¹,Chen Xing¹,Chakrabarti Chaitali¹

Affiliation:

1. School of Electrical Computer and Energy Engineering, Arizona State University,Tempe,AZ,85287

2. ASML-HMI,San Jose,CA,95134

Funder

AFRL

DARPA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9919234/9919202/09919246.pdf?arnumber=9919246

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Hierarchical Neural Network for Point Cloud Segmentation and Geometric Primitive Fitting;Entropy;2024-08-23

2. NeedLR: Streamlining Point Cloud Annotation for Enhanced Machine Learning Integration;Practice and Experience in Advanced Research Computing 2024: Human Powered Computing;2024-07-17

3. Adjustable Multi-Stream Block-Wise Farthest Point Sampling Acceleration in Point Cloud Analysis;IEEE Transactions on Circuits and Systems II: Express Briefs;2024-07

4. AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data;Proceedings of the VLDB Endowment;2024-07

5. A Method for Unseen Object Six Degrees of Freedom Pose Estimation Based on Segment Anything Model and Hybrid Distance Optimization;Electronics;2024-02-16

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