Imagen de Portada

LBNP: Learning features between neighboring points for point cloud classification

Guardado en:
Publicado en:PLoS One vol. 20, no. 1 (Jan 2025), p. e0314086
Autor principal: Wang, Lei
Otros Autores: Huang, Ming, Yang, Zhenqing, Wu, Rui, Qiu, Dashi, Xiao, Xingxing, Li, Dong, Chen, Cai
Publicado:
Public Library of Science
Materias:
Algorithms
Image processing
Learning
Cloud classification
Methods
Deep learning
Computer vision
Neighborhoods
Neural networks
Classification
Environmental
Acceso en línea:Citation/Abstract
Full Text
Full Text - PDF
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Resumen:Inspired by classical works, when constructing local relationships in point clouds, there is always a geometric description of the central point and its neighboring points. However, the basic geometric representation of the central point and its neighborhood is insufficient. Drawing inspiration from local binary pattern algorithms used in image processing, we propose a novel method for representing point cloud neighborhoods, which we call Point Cloud Local Auxiliary Block (PLAB). This module explores useful neighborhood features by learning the relationships between neighboring points, thereby enhancing the learning capability of the model. In addition, we propose a pure Transformer structure that takes into account both local and global features, called Dual Attention Layer (DAL), which enables the network to learn valuable global features as well as local features in the aggregated feature space. Experimental results show that our method performs well on both coarse- and fine-grained point cloud datasets. We will publish the code and all experimental training logs on GitHub.
ISSN:1932-6203
DOI:10.1371/journal.pone.0314086
Fuente:Health & Medical Collection