# DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration

## TL;DR

DeepICP introduces an end-to-end deep learning framework for 3D point cloud registration that matches or surpasses traditional geometric methods in accuracy, with enhanced robustness and efficiency.

## Contribution

The paper presents a novel end-to-end trainable neural network for point cloud registration that generates keypoints based on learned matching probabilities, avoiding RANSAC and improving robustness.

## Key findings

- Achieves comparable or better accuracy than state-of-the-art methods.
- Demonstrates robustness on KITTI and Apollo-SouthBay datasets.
- Provides detailed ablation and visualization analysis.

## Abstract

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.