Deep Continuous Fusion for Multi-Sensor 3D Object Detection
This work addresses the problem of accurate 3D object detection for autonomous driving systems by improving multi-sensor fusion.
This paper introduces a 3D object detector that leverages both LIDAR and camera data. It achieves significant improvements over state-of-the-art methods on both KITTI and a large-scale 3D object detection benchmark.
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.