LiSeg: Lightweight Road-object Semantic Segmentation In 3D LiDAR Scans For Autonomous Driving

被引：0

作者：

Zhang, Wenquan ^{[1
]}

Zhou, Chancheng ^{[1
]}

Yang, Junjie ^{[1
]}

Huang, Kai ^{[1
]}

机构：

[1] Sun Yat Sen Univ, Sch Data & Comp Sci, Key Lab Machine Intelligence & Adv Comp, Minist Educ, Guangzhou, Guangdong, Peoples R China

来源：

2018 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV) | 2018年

基金：

美国国家科学基金会;

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

LiDAR based perception module plays an important role in autonomous driving. However, the present CNN models are designed for image processing but not LiDAR point clouds. The performances of such models are limited by the great memory consumption and heavy computation cost. In this work, a lightweight CNN model, LiSeg, is proposed to perform real-time road-object semantic segmentation on LiDAR point cloud scans for autonomous driving. The model size of LiSeg is several times smaller than others, while achieving high accuracy.

引用

页码：1021 / 1026

页数：6

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