Reliable Normal Estimation from Sparse LiDAR Point Clouds

Autor:	Jae-Min Sa, Kang-Sun Choi, Seong-Jun Bae, Ye-Chan Choi
Rok vydání:	2020
Předmět:	Vertex (graph theory) Computer science Plane (geometry) Point cloud behavioral disciplines and activities Measure (mathematics) Vertex (geometry) body regions Lidar Face (geometry) Vertex normal Algorithm Reliability (statistics) MathematicsofComputing_DISCRETEMATHEMATICS
Zdroj:	ICCE
DOI:	10.1109/icce46568.2020.9043024
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::37ad04c7ef845d154efbf44269b53eda https://doi.org/10.1109/icce46568.2020.9043024 Zobrazit plný text záznamu