SPriorSeg: Fast Road-Object Segmentation using Deep Semantic Prior for Sparse 3D Point Clouds

被引:0
|
作者
Na, Ki-In [1 ,2 ]
Park, Byungjae [3 ]
Kim, Jong-Hwan [4 ]
机构
[1] ETRI, Intelligent Robot Res Div, Daejeon, South Korea
[2] Korea Adv Inst Sci & Technol, Robot Program, Daejeon, South Korea
[3] KOREATECH, Sch Mech Engn, Cheonan, Chungnam, South Korea
[4] Korea Adv Inst Sci & Technol, Sch Elect Engn, Daejeon, South Korea
关键词
D O I
10.1109/smc42975.2020.9282832
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Detection and classification of road-objects like cars, pedestrians, and cyclists is the first step in autonomous driving. In particular, point-wise object segmentation for 3D point clouds is essential to estimate the precise appearances of the road-objects. In this paper, we propose SPriorSeg, a fast and accurate point-level object segmentation for point clouds by integrating the strengths of deep convolutional auto-encoder and region growing algorithm. Semantic segmentation using the light-weighted convolutional auto-encoder generates semantic prior by labeling a spherical projection image of point clouds pixel-by-pixel with classes of road-objects. The region growing algorithm achieves pixel-wise instance segmentation by taking into account semantic prior and geometric features between neighboring pixels. We build a well-balanced, pixel-level labeled dataset for all classes using 3D bounding boxes and point clouds from the KITTI object dataset. The dataset is employed to train our light-weighted neural network for semantic segmentation and demonstrate the performance of both semantic and instance segmentation of SPriorSeg.
引用
收藏
页码:3928 / 3933
页数:6
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