@inproceedings{0e2e8c9a07e34c6285c0c42d0f95bf88,
title = "Deep Continuous Fusion for Multi-sensor 3D Object Detection",
abstract = "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.",
keywords = "3D object detection, Autonomous driving, Multi-sensor fusion",
author = "Ming Liang and Bin Yang and Shenlong Wang and Raquel Urtasun",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 15th European Conference on Computer Vision, ECCV 2018 ; Conference date: 08-09-2018 Through 14-09-2018",
year = "2018",
doi = "10.1007/978-3-030-01270-0_39",
language = "English (US)",
isbn = "9783030012694",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "663--678",
editor = "Yair Weiss and Vittorio Ferrari and Cristian Sminchisescu and Martial Hebert",
booktitle = "Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings",
address = "Germany",
}