Open AccessPCCN‐RE: Point cloud colourisation network based on relevance embedding
Author(s) -
Wang Feiran,
Li Xiaoqiang,
Liu Jitao
Publication year - 2022
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/cvi2.12112
Subject(s) - point cloud , relevance (law) , embedding , computer science , similarity (geometry) , pooling , artificial intelligence , point (geometry) , cloud computing , data mining , mathematics , image (mathematics) , geometry , political science , law , operating system
The colour of the point cloud provides abundant information for perception tasks such as autonomous driving and virtual reality. However, few prior work studied the automatic colourisation of colourless point clouds. In this paper, the authors propose a novel method named as Point cloud Colorization Network based on Relevance Embedding (PCCN‐RE) relying on three structures: a relevance embedding structure that efficiently captures local information through the calculation of a covariance matrix within nearby points; a weighted pooling structure designed to facilitate the fusion of features; an enhanced spatial transform network structure that keeps the invariance of input point clouds. On the ShapeNetCore dataset, our PCCN‐RE generates more authentic colour than state‐of‐the‐art methods for colourless point clouds and achieves the highest results by obtaining a Peak Signal to Noise Ratio of 9.40 and a Structural Similarity Index of 0.62.