  The perception system in autonomous vehicles is responsible for detecting and
tracking the surrounding objects. This is usually done by taking advantage of
several sensing modalities to increase robustness and accuracy, which makes
sensor fusion a crucial part of the perception system. In this paper, we focus
on the problem of radar and camera sensor fusion and propose a middle-fusion
approach to exploit both radar and camera data for 3D object detection. Our
approach, called CenterFusion, first uses a center point detection network to
detect objects by identifying their center points on the image. It then solves
the key data association problem using a novel frustum-based method to
associate the radar detections to their corresponding object's center point.
The associated radar detections are used to generate radar-based feature maps
to complement the image features, and regress to object properties such as
depth, rotation and velocity. We evaluate CenterFusion on the challenging
nuScenes dataset, where it improves the overall nuScenes Detection Score (NDS)
of the state-of-the-art camera-based algorithm by more than 12%. We further
show that CenterFusion significantly improves the velocity estimation accuracy
without using any additional temporal information. The code is available at
https://github.com/mrnabati/CenterFusion .
