  Accurate and fast 3D object detection from point clouds is a key task in
autonomous driving. Existing one-stage 3D object detection methods can achieve
real-time performance, however, they are dominated by anchor-based detectors
which are inefficient and require additional post-processing. In this paper, we
eliminate anchors and model an object as a single point--the center point of
its bounding box. Based on the center point, we propose an anchor-free
CenterNet3D network that performs 3D object detection without anchors. Our
CenterNet3D uses keypoint estimation to find center points and directly
regresses 3D bounding boxes. However, because inherent sparsity of point
clouds, 3D object center points are likely to be in empty space which makes it
difficult to estimate accurate boundaries. To solve this issue, we propose an
extra corner attention module to enforce the CNN backbone to pay more attention
to object boundaries. Besides, considering that one-stage detectors suffer from
the discordance between the predicted bounding boxes and corresponding
classification confidences, we develop an efficient keypoint-sensitive warping
operation to align the confidences to the predicted bounding boxes. Our
proposed CenterNet3D is non-maximum suppression free which makes it more
efficient and simpler. We evaluate CenterNet3D on the widely used KITTI dataset
and more challenging nuScenes dataset. Our method outperforms all
state-of-the-art anchor-based one-stage methods and has comparable performance
to two-stage methods as well. It has an inference speed of 20 FPS and achieves
the best speed and accuracy trade-off. Our source code will be released at
https://github.com/wangguojun2018/CenterNet3d.
