HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration (2107.11992v1)

Abstract: Point cloud registration is a fundamental problem in 3D computer vision. Outdoor LiDAR point clouds are typically large-scale and complexly distributed, which makes the registration challenging. In this paper, we propose an efficient hierarchical network named HRegNet for large-scale outdoor LiDAR point cloud registration. Instead of using all points in the point clouds, HRegNet performs registration on hierarchically extracted keypoints and descriptors. The overall framework combines the reliable features in deeper layer and the precise position information in shallower layers to achieve robust and precise registration. We present a correspondence network to generate correct and accurate keypoints correspondences. Moreover, bilateral consensus and neighborhood consensus are introduced for keypoints matching and novel similarity features are designed to incorporate them into the correspondence network, which significantly improves the registration performance. Besides, the whole network is also highly efficient since only a small number of keypoints are used for registration. Extensive experiments are conducted on two large-scale outdoor LiDAR point cloud datasets to demonstrate the high accuracy and efficiency of the proposed HRegNet. The project website is https://ispc-group.github.io/hregnet.

• The paper presents a hierarchical framework that extracts and refines multi-layer keypoints for robust outdoor LiDAR registration.
• It employs bilateral and neighborhood consensus mechanisms to generate reliable keypoint correspondences, significantly minimizing registration errors.
• Experimental evaluations on KITTI and NuScenes datasets show HRegNet’s superior accuracy and computational efficiency, paving the way for real-time autonomous driving applications.

HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration

This essay provides an expert overview of the research paper titled "HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration." The paper presents HRegNet, a novel approach designed to address the complexities inherent in registering large-scale outdoor LiDAR point clouds, a fundamental task within 3D computer vision with significant implications for applications such as robotics and autonomous driving.

Research Context and Challenges

Point cloud registration is crucial for estimating the rigid transformation between two point clouds. Traditional methods like Iterative Closest Point (ICP) are reliant on initial transformation estimates and often succumb to local minima due to the problem's non-convexity. The adaptation of ICP and other classical approaches to large-scale outdoor LiDAR data poses additional computational challenges due to the variability and complexity of the data. Existing deep learning-based registration methods predominantly focus on object-level or indoor point clouds and tend to be inefficient or unreliable when applied to outdoor LiDAR data.

HRegNet Methodology

HRegNet introduces a hierarchical keypoint-based network architecture tailored for large-scale outdoor LiDAR point clouds. The network's architecture strategically combines the strength of keypoints and descriptors across multiple layers to achieve robust and precise registration.

1. Hierarchical Framework: HRegNet operates on hierarchically extracted keypoints and descriptors from the point clouds' sparse data. This hierarchical method ensures that the reliable features from deeper layers and the exact positional information from shallower layers are effectively utilized.
2. Correspondence Network: A learning-based network is proposed to generate accurate keypoint correspondences and reject unreliable matches, emphasizing bilateral and neighborhood consensus. Bilateral consensus ensures that corresponding keypoints are each other's nearest neighbors in descriptor space, while neighborhood consensus ensures their surrounding keypoints also exhibit high similarity.
3. Coarse to Fine Registration: The process begins with a coarse registration that operates on globally matched keypoints in deeper layers, refined subsequently through fine registration leveraging spatial neighborhood information in shallower layers.

Experimental Evaluation

HRegNet's performance was validated with extensive experiments on two prominent LiDAR point cloud datasets: the KITTI odometry dataset and the NuScenes dataset. The results highlight the network’s superior accuracy and operational efficiency over classical methods like ICP and learning-based alternatives, including DCP and DGR. HRegNet not only achieved lower registration errors but also demonstrated significant computational efficiency due to its keypoint-centric registration strategy.

Implications and Future Directions

The development of HRegNet represents an advancement in handling the complexities of outdoor LiDAR point cloud registration, offering a scalable and efficient solution with potential for real-time applications such as autonomous driving. The hierarchical approach could inspire further research into adaptive keypoint selection and feature extraction techniques that better accommodate diverse and complex data distributions. Moreover, the incorporation of consensus strategies within correspondence networks can be extended to other registration or matching tasks across various vision applications.

Overall, the contribution of HRegNet to the field of computer vision lies in its methodical addressing of large-scale point cloud registration challenges, paving the way for subsequent explorations and refinements in both algorithmic and application-driven contexts.