Paper Info
Title
Joint 3d Instance Segmentation And Object Detection For Autonomous Driving
Abstract
Currently, in Autonomous Driving (AD), most of the 3D object detection frameworks (either anchor- or anchor-free-based) consider the detection as a Bounding Box (BBox) regression problem. However, this compact representation is not sufficient to explore all the information of the objects. To tackle this problem, we propose a simple but practical detection framework to jointly predict the 3D BBox and instance segmentation. For instance segmentation, we propose a Spatial Embeddings (SEs) strategy to assemble all foreground points into their corresponding object centers. Base on the SE results, the object proposals can be generated based on a simple clustering strategy. For each cluster, only one proposal is generated. Therefore, the Non-Maximum Suppression (NMS) process is no longer needed here. Finally, with our proposed instance-aware ROI pooling, the BBox is refined by a second-stage network. Experimental results on the public KITTI dataset show that the proposed SEs can significantly improve the instance segmentation results compared with other feature embedding-based method. Meanwhile, it also outperforms most of the 3D object detectors on the KITTI testing benchmark.
Year
DOI
Venue
2020
10.1109/CVPR42600.2020.00191
2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
DocType
ISSN
Citations 
Conference
1063-6919
0
PageRank 
References 
Authors
0.34
32
8
Name
Order
Citations
PageRank
Dingfu Zhou18611.23
Jin Fang2287.66
Xibin Song31218.84
Liu Liu4163.64
Junbo Yin5133.91
Yuchao Dai641842.03
Hongdong Li71724101.81
Ruigang Yang83675226.03