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Deep Continuous Fusion for Multi-sensor 3D Object Detection

  • Ming Liang
  • Bin Yang
  • Shenlong Wang
  • Raquel Urtasun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11220)

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 Multi-sensor fusion Autonomous driving 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Ming Liang
    • 1
  • Bin Yang
    • 1
    • 2
  • Shenlong Wang
    • 1
    • 2
  • Raquel Urtasun
    • 1
    • 2
  1. 1.Uber Advanced Technologies GroupPittsburghUSA
  2. 2.University of TorontoTorontoCanada

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