YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection

• URL: http://arxiv.org/abs/2103.09422v1
• Date: Wed, 17 Mar 2021 03:43:54 GMT
• Title: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection
• Authors: Yuxuan Liu, Lujia Wang, Ming Liu
• Abstract summary: YOLOStereo3D is trained on one single GPU and runs at more than ten fps. It demonstrates performance comparable to state-of-the-art stereo 3D detection frameworks without usage of LiDAR data.
• Score: 6.5702792909006735
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Object detection in 3D with stereo cameras is an important problem in computer vision, and is particularly crucial in low-cost autonomous mobile robots without LiDARs. Nowadays, most of the best-performing frameworks for stereo 3D object detection are based on dense depth reconstruction from disparity estimation, making them extremely computationally expensive. To enable real-world deployments of vision detection with binocular images, we take a step back to gain insights from 2D image-based detection frameworks and enhance them with stereo features. We incorporate knowledge and the inference structure from real-time one-stage 2D/3D object detector and introduce a light-weight stereo matching module. Our proposed framework, YOLOStereo3D, is trained on one single GPU and runs at more than ten fps. It demonstrates performance comparable to state-of-the-art stereo 3D detection frameworks without usage of LiDAR data. The code will be published in https://github.com/Owen-Liuyuxuan/visualDet3D.

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