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Open AccessArticle

Slice-Based Instance and Semantic Segmentation for Low-Channel Roadside LiDAR Data

by 1, 1, 2 and 1,3,*
1
Department of Traffic Information and Control Engineering, Jilin University, Changchun 130022, China
2
Texas A&M Transportation Institute, Texas A&M University, College Station, TX 77843, USA
3
Jilin Engineering Research Center for ITS, Changchun 130022, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(22), 3830; https://doi.org/10.3390/rs12223830
Received: 28 September 2020 / Revised: 7 November 2020 / Accepted: 20 November 2020 / Published: 21 November 2020
(This article belongs to the Section Urban Remote Sensing)
More and more scholars are committed to light detection and ranging (LiDAR) as a roadside sensor to obtain traffic flow data. Filtering and clustering are common methods to extract pedestrians and vehicles from point clouds. This kind of method ignores the impact of environmental information on traffic. The segmentation process is a crucial part of detailed scene understanding, which could be especially helpful for locating, recognizing, and classifying objects in certain scenarios. However, there are few studies on the segmentation of low-channel (16 channels in this paper) roadside 3D LiDAR. This paper presents a novel segmentation (slice-based) method for point clouds of roadside LiDAR. The proposed method can be divided into two parts: the instance segmentation part and semantic segmentation part. The part of the instance segmentation of point cloud is based on the regional growth method, and we proposed a seed point generation method for low-channel LiDAR data. Furthermore, we optimized the instance segmentation effect under occlusion. The part of semantic segmentation of a point cloud is realized by classifying and labeling the objects obtained by instance segmentation. For labeling static objects, we represented and classified a certain object through the related features derived from its slices. For labeling moving objects, we proposed a recurrent neural network (RNN)-based model, of which the accuracy could be up to 98.7%. The result implies that the slice-based method can obtain a good segmentation effect and the slice has good potential for point cloud segmentation. View Full-Text
Keywords: slice-based segmentation; roadside LiDAR; instance and semantic segmentation; 3D point cloud slice-based segmentation; roadside LiDAR; instance and semantic segmentation; 3D point cloud
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Liu, H.; Lin, C.; Wu, D.; Gong, B. Slice-Based Instance and Semantic Segmentation for Low-Channel Roadside LiDAR Data. Remote Sens. 2020, 12, 3830.

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