Paper Info
Title
Pointwise Geometric And Semantic Learning Network On 3d Point Clouds
Abstract
The geometric and semantic information of 3D point clouds significantly influence the analysis of 3D point cloud structures. However, semantic learning of 3D point clouds based on deep learning is challenging due to the naturally unordered data structure. In this work, we strive to impart machines with the knowledge of 3D object shapes, thereby enabling them to infer the high-level semantic information from the 3D model. Inspired by the vector of locally aggregated descriptors, we propose indirectly describing the high-level semantic information by associating each point's low-level geometric descriptor with a few visual words. Based on this approach, we design an end-to-end network for 3D shape analysis that combines pointwise low-level geometric and high-level semantic information. The network includes a spatial transform and a uniform operation that make it invariant to input rotation and translation, respectively. Our network also employs pointwise feature extraction and pooling operations to solve the unordered point cloud problem. In a series of experiments with popular 3D shape analysis benchmarks, our network exhibits competitive performance on many important tasks, such as 3D object classification, 3D object part segmentation, semantic segmentation in scenes, and commercial 3D CAD model retrieval.
Year
DOI
Venue
2020
10.3233/ICA-190608
INTEGRATED COMPUTER-AIDED ENGINEERING
Keywords
Field
DocType
3D point clouds, convolutional neural network, object classification, semantic segmentation, shape retrieval
Network on,Computer science,Semantic learning,Artificial intelligence,Point cloud,Machine learning,Pointwise
Journal
Volume
Issue
ISSN
27
1
1069-2509
Citations 
PageRank 
References 
2
0.36
0
Authors
5
Name
Order
Citations
PageRank
Dejun Zhang123819.97
Fazhi He254041.02
Zhigang Tu38511.72
Lu Zou420.36
Yi-Lin Chen51449.13