HUSC: a local feature descriptor of point cloud based on hemisphere neighborhood

Bo Jiang; Yanxing Ma; Feng He; Ke Xu; Jianwei Wan

doi:10.1117/12.2600154

1 June 2021 HUSC: a local feature descriptor of point cloud based on hemisphere neighborhood

Bo Jiang, Yanxing Ma, Feng He, Ke Xu, Jianwei Wan

Proceedings Volume 11848, International Conference on Signal Image Processing and Communication (ICSIPC 2021); 1184816 (2021) https://doi.org/10.1117/12.2600154
Event: International Conference on Signal Image Processing and Communication (ICSIPC 2021), 2021, Chengdu, China

Abstract

In order to improve the efficiency of LiDAR point cloud object recognition and reduce the computational overhead, a new feature descriptor, Hemispheric Unique Shape Context (HUSC), is presented in this paper by using an improved neighborhood determination method. Firstly, the normal vector and tangent plane at key point are estimated and the local reference frame is established. Then a hemispherical neighborhood is constructed based on the tangent plane and divided into bins according to azimuth, polar angle and radial direction. Finally, the points in each bin are counted and the local feature descriptors of key points are obtained. HUSC feature descriptor can not only ensure the discriminability of descriptors, but also improve the efficiency of object recognition by reducing the number of free bins. Experiments on Bologna dataset and 3DMatch dataset show that HUSC feature descriptor with hemispheric neighborhood is robust to noise, occupying less memory and operating faster.

Citation Download Citation

Bo Jiang, Yanxing Ma, Feng He, Ke Xu, and Jianwei Wan "HUSC: a local feature descriptor of point cloud based on hemisphere neighborhood", Proc. SPIE 11848, International Conference on Signal Image Processing and Communication (ICSIPC 2021), 1184816 (1 June 2021); https://doi.org/10.1117/12.2600154

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available