Deep Continuous Fusion for Multi-Sensor 3D Object Detection

• URL: http://arxiv.org/abs/2012.10992v1
• Date: Sun, 20 Dec 2020 18:43:41 GMT
• Title: Deep Continuous Fusion for Multi-Sensor 3D Object Detection
• Authors: Ming Liang, Bin Yang, Shenlong Wang, Raquel Urtasun
• Abstract summary: We propose a novel 3D object detector that can exploit both LIDAR as well as cameras to perform very accurate localization. We design an end-to-end learnable architecture that exploits continuous convolutions to fuse image and LIDAR feature maps at different levels of resolution.
• Score: 103.5060007382646
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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