The KITTI Vision Benchmark Suite

This paper presents a novel approach for joint
object detection and orientation estimation in
a single deep convolutional neural network
utilizing proposals calculated from 3D data.
For orientation estimation, we extend a R-CNN
like architecture by several carefully designed
layers. Two new object proposal methods are
introduced, to make use of stereo as well as
lidar data. Our experiments on the KITTI
dataset show that by combining proposals of
both domains, high recall can be achieved while
keeping the number of proposals low.
Furthermore, our method for joint detection and
orientation estimation outperforms state of the
art approaches for cyclists on the easy test
scenario of the KITTI test dataset.

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@inproceedings{braun2016pose,
title={Pose-RCNN: Joint object detection and
pose estimation using 3D object proposals},
author={Braun, Markus and Rao, Qing and Wang,
Yikang and Flohr, Fabian},
booktitle={Intelligent Transportation Systems
(ITSC), 2016 IEEE 19th International Conference
on},
pages={1546--1551},
year={2016},
organization={IEEE}
}