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Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection
[article]
2021
arXiv
pre-print
The objective of this paper is to learn context- and depth-aware feature representation to solve the problem of monocular 3D object detection. We make following contributions: (i) rather than appealing to the complicated pseudo-LiDAR based approach, we propose a depth-conditioned dynamic message propagation (DDMP) network to effectively integrate the multi-scale depth information with the image context;(ii) this is achieved by first adaptively sampling context-aware nodes in the image context
arXiv:2103.16470v1
fatcat:3zikeoajn5fwjf3xz6oipoopre