3D Object Proposals for Accurate Object Class Detection
Xiaozhi Chen, Kaustav Kundu, Yukun Zhu, Andrew G Berneshawi, Huimin Ma, Sanja Fidler, Raquel Urtasun
Abstract
The goal of this paper is to generate high-quality 3D object proposals in the context of autonomous driving. Our method exploits stereo imagery to place proposals 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 outperforms all existing results on all three KITTI object classes.
BibTeX
@inproceedings{NIPS2015_6da37dd3,
author = {Chen, Xiaozhi and Kundu, Kaustav and Zhu, Yukun and Berneshawi, Andrew G and Ma, Huimin and Fidler, Sanja and Urtasun, Raquel},
booktitle = {Advances in Neural Information Processing Systems},
editor = {C. Cortes and N. Lawrence and D. Lee and M. Sugiyama and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {3D Object Proposals for Accurate Object Class Detection},
url = {https://proceedings.neurips.cc/paper_files/paper/2015/file/6da37dd3139aa4d9aa55b8d237ec5d4a-Paper.pdf},
volume = {28},
year = {2015}
}