Monocular 3D Object Detection for Autonomous Driving

Abstract

The goal of this paper is to perform 3D object detection in single monocular images in the domain of autonomous driving. Our method first aims to generate a set of candidate class-specific object proposals, which are then run through a standard CNN pipeline to obtain high-quality object detections. The focus of this paper is on proposal generation. In particular, we propose a probabilistic model that places object candidates in 3D using a prior on ground-plane. We then score each candidate box projected to the image plane via several intuitive potentials such as semantic segmentation, contextual information, size and location priors and typical object shape. The weights in our model are trained with S-SVM. Experiments show that our object proposal generation approach significantly outperforms all monocular baselines, and achieves the best detection performance on the challenging KITTI benchmark, among the published monocular competitors.