NDLI: PUConv: Upsampling convolutional network for point cloud semantic segmentation

Content Provider	IET Digital Library
Author	Lu, Jian Cheng, Haozhe Luo, Maoxin Liu, Tong Zhang, Kaibing
Abstract	Due to the issue of disorder, it is difficult to directly utilise a 2D convolutional neural networks to process 3D point clouds. Recently, PointNet can directly use 3D point sets as the input of convolutional neural networks and complete the processing of point clouds with multi-layer perceptron (MLP) and symmetric functions. However, the use of MLP to compute the weight function ignores the problem of non-uniformity sampling caused by the density of point set data. To address the above problem, based on the PointNet++ structure, a kernel density estimation based method is proposed to calculate the density level of the local point sets region under the optimal bandwidth selection principle, and the density re-weighting of the weight function is developed to better fit the structure of local point clouds. In addition, the authors utilise the upsampling convolution operation to avoid duplicate storages and calculations, making the point cloud reconstruction more efficient. The experiments carried out the semantic segmentation on both the synthetic data and the real indoor scenes show that the proposed method is capable of obtaining promising semantic segmentation results.
Starting Page	435
Ending Page	438
Page Count	4
ISSN	00135194
Volume Number	56
e-ISSN	1350911X
Issue Number	Issue 9, Apr (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/56/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2019.3705
Journal	Electronics Letters
Publisher Date	2020-02-19
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computational Geometry Computer Vision And Image Processing Technique Convolutional Neural Nets Convolutional Neural Network Density Level Graphics Technique Image Colour Analysis Image Reconstruction Image Segmentation Kernel Density Estimation Based Method Local Point Clouds MLP Multilayer Perceptron Multilayer Perceptrons Neural Computing Technique Nonuniformity Sampling Optical, Image And Video Signal Processing Optimal Bandwidth Selection Principle Point Cloud Reconstruction Point Cloud Semantic Segmentation Point Set Data PointNet++ Structure Real Indoor Scenes Robot Vision Symmetric Function Synthetic Data Upsampling Convolution Operation Weight Function
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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