3D Object Proposals for Accurate Object Class Detection

People

Xiaozhi Chen, Kaustav Kundu, Yukun Zhu, Andrew 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.

Paper

Xiaozhi Chen*, Kaustav Kundu*, Yukun Zhu, Andrew Berneshawi, Huimin Ma, Sanja Fidler, Raquel Urtasun.
3D Object Proposals for Accurate Object Class Detection
Neural Information Processing Systems (NIPS), Montreal, Canada, 2015
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* Denotes equal contribution

Code and Data

Please visit the downloads page.

Results

Car Results

Car, Pedestrian and Cyclist Results

Acknowledgements

The work was partially supported by NSFC 61171113, NSERC and Toyota Motor Corporation.

Contact

For questions regarding the data or code, please contact Xiaozhi Chen, Kaustav Kundu, Sanja Fidler and Raquel Urtasun.