Journal
2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
Volume -, Issue -, Pages 1836-1846Publisher
IEEE
DOI: 10.1109/CVPR42600.2020.00191
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Funding
- Natural Science Foundation of China [61871325, 61420106007, 61671387]
- National Key Research and Development Program of China [2018AAA0102803]
- ARC Centre of Excellence for Robotics Vision [CE140100016]
- ARC-Discovery grant [DP 190102261]
- ARC-LIEF grant [190100080]
- Baidu Research, Robotics and Autonomous Driving Laboratory (RAL)
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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.
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