3D object proposals for accurate object class detection
Xiaozhi Chen, Kaustav Kundu, Yukun Zhu, Andrew Berneshawi, Huimin Ma, Sanja Fidler, Raquel Urtasun · 2015
The goal of this paper is to generate high-quality 3D object proposals in the con-text of autonomous driving. Our method exploits stereo imagery to place propos-als in the form of 3D bounding boxes. We formulate the problem as minimizing an energy function encoding object size priors, ground plane as well as several depth informed features that reason about free space, point cloud densities and distance to the ground. Our experiments show significant performance gains over existing RGB and RGB-D object proposal methods on the challenging KITTI benchmark. Combined with convolutional neural net (CNN) scoring, our approach outper-forms all existing results on all three KITTI object classes. 1