  We present DeepICP - a novel end-to-end learning-based 3D point cloud
registration framework that achieves comparable registration accuracy to prior
state-of-the-art geometric methods. Different from other keypoint based methods
where a RANSAC procedure is usually needed, we implement the use of various
deep neural network structures to establish an end-to-end trainable network.
Our keypoint detector is trained through this end-to-end structure and enables
the system to avoid the inference of dynamic objects, leverages the help of
sufficiently salient features on stationary objects, and as a result, achieves
high robustness. Rather than searching the corresponding points among existing
points, the key contribution is that we innovatively generate them based on
learned matching probabilities among a group of candidates, which can boost the
registration accuracy. Our loss function incorporates both the local similarity
and the global geometric constraints to ensure all above network designs can
converge towards the right direction. We comprehensively validate the
effectiveness of our approach using both the KITTI dataset and the
Apollo-SouthBay dataset. Results demonstrate that our method achieves
comparable or better performance than the state-of-the-art geometry-based
methods. Detailed ablation and visualization analysis are included to further
illustrate the behavior and insights of our network. The low registration error
and high robustness of our method makes it attractive for substantial
applications relying on the point cloud registration task.
