The KITTI Vision Benchmark Suite

In this paper, we aim at addressing two critical issues in the 3D detection task, including the exploitation of multiple sensors~(namely LiDAR point cloud and camera image), as well as the inconsistency between the localization and classification confidence. To this end, we propose a novel fusion module to enhance the point features with semantic image features in a point-wise manner without any image annotations. Besides, a consistency enforcing loss is employed to explicitly encourage the consistency of both the localization and classification confidence. We design an end-to-end learnable framework named EPNet to integrate these two components. Extensive experiments on the KITTI and SUN-RGBD datasets demonstrate the superiority of EPNet over the state-of-the-art methods.
\url{https://github.com/happinesslz/EPNet}.

TBA

@misc{huang2020epnet,
title={EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection},
author={Tengteng Huang and Zhe Liu and Xiwu Chen and Xiang Bai},
year={ECCV 2020},
eprint={2007.08856},
archivePrefix={arXiv},
primaryClass={cs.CV}
}