Paper Info
Title
A Multi-task Learning Framework for Semantic Segmentation in MLS Point Clouds
Abstract
We study a multi-task learning framework for semantic segmentation in Mobile Laser Scanning (MLS) point clouds. The existing methods on semantic segmentation of point cloud rely on a large number of annotation data. However, manually annotating data is time-consuming and laborious, and the manually annotation efficiency is particularly low. To alleviate those problems, we propose to exploit a multi-task learning framework to reduce the large demand of training samples for implementing semantic labeling of point clouds. Specifically, we design a new neural network containing a backbone network and two branching networks, which accomplish the color prediction and category prediction, respectively. Color prediction, as an auxiliary task, can be easily conducted by exploiting the color information of each 3D point to train the proposed neural network. Here, color information of each point can be easily generated by using the optical images obtained by the cameras equipped in the MLS system. Once the training procedure of color prediction is completed, we only use a small portion of manually-annotated points to fine-tune the branching network of category prediction for each 3D point. To demonstrate the effectiveness and correctness of our proposed framework, we conducted extensive experiments on the colorized point clouds which are collected by a RIEGL VMX450 MLS system. The experimental results show the proposed approach can reach 96.04%. OA and 94.41% mIoU under the supervision of 10% annotation data.
Year
DOI
Venue
2022
10.1007/978-3-031-06794-5_31
Artificial Intelligence and Security
Keywords
DocType
ISSN
Point cloud, Semantic segmentation, Multi-task learning
Conference
0302-9743
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Lin Xi100.34
Luo Huan200.34
Guo Wenzhong300.34
Cheng Wang411829.56
Jonathan Li5798119.18