Multivariate Probabilistic Monocular 3D Object Detection

Xuepeng Shi, Zhixiang Chen, Tae-Kyun Kim; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023, pp. 4281-4290

In autonomous driving, monocular 3D object detection is an important but challenging task. Towards accurate monocular 3D object detection, some recent methods recover the distance of objects from the physical height and visual height of objects. Such decomposition framework can introduce explicit constraints on the distance prediction, thus improving its accuracy and robustness. However, the inaccurate physical height and visual height prediction still may exacerbate the inaccuracy of the distance prediction. In this paper, we improve the framework by multivariate probabilistic modeling. We explicitly model the joint probability distribution of the physical height and visual height. This is achieved by learning a full covariance matrix of the physical height and visual height during training, with the guide of a multivariate likelihood. Such explicit joint probability distribution modeling not only leads to robust distance prediction when both the predicted physical height and visual height are inaccurate, but also brings learned covariance matrices with expected behaviors. The experimental results on the challenging Waymo Open and KITTI datasets show the effectiveness of our framework.