mlsanityData to 7 Oct 2026

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

arXiv 2201.04341 ↗First posted 12 Jan 2022cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 40 of its 51 references that are in the map. First posted on arXiv 12 Jan 2022.

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40 of 51 references.

References · 40

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28 more
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