MFFRand: Semantic Segmentation of Point Clouds Based on Multi-Scale Feature Fusion and Multi-Loss Supervision
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Published:2022-11-07
Issue:21
Volume:11
Page:3626
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ISSN:2079-9292
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Container-title:Electronics
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language:en
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Short-container-title:Electronics
Author:
Miao ZhiqingORCID, Song ShaojingORCID, Tang Pan, Chen JianORCID, Hu Jinyan, Gong Yumei
Abstract
With the application of the random sampling method in the down-sampling of point clouds data, the processing speed of point clouds has been greatly improved. However, the utilization of semantic information is still insufficient. To address this problem, we propose a point cloud semantic segmentation network called MFFRand (Multi-Scale Feature Fusion Based on RandLA-Net). Based on RandLA-Net, a multi-scale feature fusion module is developed, which is stacked by encoder-decoders with different depths. The feature maps extracted by the multi-scale feature fusion module are continuously concatenated and fused. Furthermore, for the network to be trained better, the multi-loss supervision module is proposed, which could strengthen the control of the training process of the local structure by adding sub-losses in the end of different decoder structures. Moreover, the trained MFFRand network could be connected to the inference network by different decoder terminals separately, which could achieve the inference of different depths of the network. Compared to RandLA-Net, MFFRand has improved mIoU on both S3DIS and Semantic3D datasets, reaching 71.1% and 74.8%, respectively. Extensive experimental results on the point cloud dataset demonstrate the effectiveness of our method.
Funder
Shanghai Intelligent Manufacturing Collaborative Logistics Equipment Engineering Technology Research Center Collaborative Innovation Platform of Electronic Information Master of Shanghai Polytechnic University
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
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