  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.
