MDS-Net: A Multi-scale Depth Stratification Based Monocular 3D Object Detection Algorithm

• URL: http://arxiv.org/abs/2201.04341v1
• Date: Wed, 12 Jan 2022 07:11:18 GMT
• Title: MDS-Net: A Multi-scale Depth Stratification Based Monocular 3D Object Detection Algorithm
• Authors: Zhouzhen Xie, Yuying Song, Jingxuan Wu, Zecheng Li, Chunyi Song, Zhiwei Xu
• Abstract summary: This paper proposes a one-stage monocular 3D object detection algorithm based on multi-scale depth stratification. Experiments on the KITTI benchmark show that the MDS-Net outperforms the existing monocular 3D detection methods in 3D detection and BEV detection tasks.
• Score: 4.958840734249869
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Monocular 3D object detection is very challenging in autonomous driving due to the lack of depth information. This paper proposes a one-stage monocular 3D object detection algorithm based on multi-scale depth stratification, which uses the anchor-free method to detect 3D objects in a per-pixel prediction. In the proposed MDS-Net, a novel depth-based stratification structure is developed to improve the network's ability of depth prediction by establishing mathematical models between depth and image size of objects. A new angle loss function is then developed to further improve the accuracy of the angle prediction and increase the convergence speed of training. An optimized soft-NMS is finally applied in the post-processing stage to adjust the confidence of candidate boxes. Experiments on the KITTI benchmark show that the MDS-Net outperforms the existing monocular 3D detection methods in 3D detection and BEV detection tasks while fulfilling real-time requirements.

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