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.
@article{arxiv.2201.04341,
title = {MDS-Net: A Multi-scale Depth Stratification Based Monocular 3D Object Detection Algorithm},
author = {Zhouzhen Xie and Yuying Song and Jingxuan Wu and Zecheng Li and Chunyi Song and Zhiwei Xu},
journal= {arXiv preprint arXiv:2201.04341},
year = {2022}
}