DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration (1905.04153v2)

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.

• The paper introduces DeepICP, an end-to-end deep learning framework that integrates local feature extraction with global geometric constraints to enhance 3D registration accuracy.
• The system employs PointNet++ for semantic feature extraction, effectively identifying stable keypoints while reducing the impact of dynamic noise.
• It leverages a novel probability-based keypoint generation strategy and tailored loss function, resulting in minimal registration errors on benchmark datasets.

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

The paper presents DeepICP, an end-to-end learning-based framework for 3D point cloud registration, which seeks to align disparate point clouds by estimating the relative transformations between them. Unlike traditional keypoint-based methods requiring RANSAC procedures, DeepICP innovatively employs deep neural networks to streamline the process through an integrated, trainable network. This framework achieves robust registration accuracy comparable to state-of-the-art geometric methods using datasets such as KITTI and Apollo-SouthBay.

Key Contributions

1. End-to-End Framework: DeepICP represents a significant development in transitioning point cloud registration tasks into a fully neural network-driven domain. The paper underscores the role of end-to-end learning, allowing the model to automatically optimize for both local feature distinctiveness and global geometric coherence.
2. Semantic and Salient Feature Extraction: Utilizing PointNet++ for feature extraction, the system identifies key points that are essential for registration by focusing on stationary objects while avoiding interference from dynamic components. The embedded features convey semantic information, enabling the network to emphasize prominent yet stable object features necessary for accurate registration.
3. Innovative Keypoint and Corresponding Point Generation: The framework introduces a novel approach where corresponding points are generated, not merely matched, by leveraging learned probabilities among candidate points. This improves registration accuracy by resolving issues like local sparsity, which traditionally amplify error margins in similar geometric methods.
4. Loss Function Design: The loss function architecturally unifies local similarity with global geometric constraints, ensuring convergence towards precise solution vectors—a fundamental component to the framework's trainability and performance consistency.

Experimentation and Results

The effectiveness of DeepICP is validated through comprehensive testing on established benchmarks, notably the KITTI and Apollo-SouthBay datasets. Numerical results illustrate that DeepICP achieves remarkable robustness and precision in registration tasks, which is often challenging given the local sparsity and dynamic noise characteristic of LiDAR point cloud data. Specifically, the system demonstrates minimal registration errors and exhibits superiority in robustness compared to classical methods like ICP and its variations.

The ablation studies confirm the necessity and contributory impact of each architectural component, such as deep feature embedding and the incorporation of novel loss parameters, providing insights into the necessary components for optimized registration performance.

Implications and Future Work

DeepICP demonstrates a significant shift towards applying deep learning techniques to the domain of 3D point cloud registration, highlighting how learning-based approaches can rival traditional geometric methods previously deemed unmatched in this space. Its accuracy and robustness suggest that such frameworks could profoundly influence real-time applications, such as autonomous vehicle navigation, 3D reconstruction, and SLAM, where precision mapping and localization are crucial.

Future work could extend this framework within varied sensor modalities or enhance methodologies for adaptive learning across diverse environmental conditions (e.g., different weather or lighting scenarios). The flexibility of end-to-end learning models also opens prospects for continual learning and real-time updates during operation, paving the way for more autonomous, responsive systems.