Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation

• URL: http://arxiv.org/abs/2004.03572v1
• Date: Tue, 7 Apr 2020 17:48:45 GMT
• Title: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation
• Authors: Jiaming Sun, Linghao Chen, Yiming Xie, Siyu Zhang, Qinhong Jiang, Xiaowei Zhou, Hujun Bao
• Abstract summary: We propose a novel system named Disp R-CNN for 3D object detection from stereo images. We use a statistical shape model to generate dense disparity pseudo-ground-truth without the need of LiDAR point clouds. Experiments on the KITTI dataset show that, even when LiDAR ground-truth is not available at training time, Disp R-CNN achieves competitive performance and outperforms previous state-of-the-art methods by 20% in terms of average precision.
• Score: 51.17232267143098
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a novel system named Disp R-CNN for 3D object detection from stereo images. Many recent works solve this problem by first recovering a point cloud with disparity estimation and then apply a 3D detector. The disparity map is computed for the entire image, which is costly and fails to leverage category-specific prior. In contrast, we design an instance disparity estimation network (iDispNet) that predicts disparity only for pixels on objects of interest and learns a category-specific shape prior for more accurate disparity estimation. To address the challenge from scarcity of disparity annotation in training, we propose to use a statistical shape model to generate dense disparity pseudo-ground-truth without the need of LiDAR point clouds, which makes our system more widely applicable. Experiments on the KITTI dataset show that, even when LiDAR ground-truth is not available at training time, Disp R-CNN achieves competitive performance and outperforms previous state-of-the-art methods by 20% in terms of average precision.

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