3D Object Proposals for Accurate Object Class Detection
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.
Cite
Text
Chen et al. "3D Object Proposals for Accurate Object Class Detection." Neural Information Processing Systems, 2015.Markdown
[Chen et al. "3D Object Proposals for Accurate Object Class Detection." Neural Information Processing Systems, 2015.](https://mlanthology.org/neurips/2015/chen2015neurips-3d/)BibTeX
@inproceedings{chen2015neurips-3d,
title = {{3D Object Proposals for Accurate Object Class Detection}},
author = {Chen, Xiaozhi and Kundu, Kaustav and Zhu, Yukun and Berneshawi, Andrew G and Ma, Huimin and Fidler, Sanja and Urtasun, Raquel},
booktitle = {Neural Information Processing Systems},
year = {2015},
pages = {424-432},
url = {https://mlanthology.org/neurips/2015/chen2015neurips-3d/}
}