Reliable Normal Estimation from Sparse LiDAR Point Clouds

In this paper, we present a reliable vertex normal estimation method from sparse point clouds that improves the accuracy of plane-based frame-to-frame registration. We define a face normal reliability measure. The vertex normals are calculated by weighted averaging adjacent face normals based on the...

Full description

Saved in:
Bibliographic Details
Main Authors: Sa, Jae-Min, Bae, Seong-Jun, Choi, Ye-Chan, Choi, Kang-Sun
Format: Conference Proceeding
Language:English
Subjects:
Conferences
Consumer electronics
Estimation
Faces
Laser radar
Point cloud compression
Reliability
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, we present a reliable vertex normal estimation method from sparse point clouds that improves the accuracy of plane-based frame-to-frame registration. We define a face normal reliability measure. The vertex normals are calculated by weighted averaging adjacent face normals based on the reliability. Through the experiments, it is confirmed that the proposed method produces consistent and reliable vertex normals.
ISSN:2158-4001
DOI:10.1109/ICCE46568.2020.9043024