Pei Liu, Guorun Yang, Peixuan Li, Zhe Wang, Jianping Shi, Zhidong Deng and Yu Qiao

Monocular 3D object detection is an important and challenging task in autonomous driving. Due to the ill-posed nature of 3D detection, recent studies use prior knowledge of object categories to estimate 3D parameters. However, for each object category in real driving scenes, there exist a couple of sub-categories with different shapes (i.e. length, width, and height). For example, vehicle generally contains the sub-categories of car, van, and truck. Obviously, single prior knowledge cannot cover such diverse sub-categories. In this paper, we propose MP-Mono that exploits multiple priors to improve object detection. Specifically, a data-heuristic strategy is presented to generate multiple 3D proposals, in which we leverage the unsupervised algorithm to cluster potential sub-categories from realistic datasets, and a height-guided inference policy is used to determine the initial distances of proposals, reducing the difficulty of network learning. Additionally, we propose a local-ground guiding method that learns local depth information to enhance monocular 3D detection. The experimental results on the KITTI dataset demonstrate that our MP-Mono achieves competitive performances compared to other monocular methods, verifying the effectiveness of multi-prior integration and local-ground guiding.