# MDS-Net: A Multi-scale Depth Stratification Based Monocular 3D Object Detection Algorithm Source: [MDS-Net: A Multi-scale Depth Stratification Based Monocular 3D Object Detection Algorithm]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:10:48.263Z Authors: Zhouzhen Xie, Yuying Song, Jingxuan Wu, Zecheng Li, Chunyi Song, Zhiwei Xu Published: 2022-01-12T07:11:18.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()