In this paper, we propose a novel 3D object detector that can exploit both LIDAR as well as cameras to perform very accurate localization. Towards this goal, we design an end-to-end learnable architecture that exploits continuous convolutions to fuse image and LIDAR feature maps at different levels of resolution. Our proposed continuous fusion layer encode both discrete-state image features as well as continuous geometric information. This enables us to design a novel, reliable and efficient end-to-end learnable 3D object detector based on multiple sensors. Our experimental evaluation on both KITTI as well as a large scale 3D object detection benchmark shows significant improvements over the state of the art.
@article{arxiv.2012.10992,
title = {Deep Continuous Fusion for Multi-Sensor 3D Object Detection},
author = {Ming Liang and Bin Yang and Shenlong Wang and Raquel Urtasun},
journal= {arXiv preprint arXiv:2012.10992},
year = {2020}
}