DeepCLR: Correspondence-Less Architecture for Deep End-to-End Point Cloud Registration
This addresses the problem of efficient and accurate point cloud registration for applications like LiDAR-based navigation, though it is incremental as it builds on existing deep learning methods.
The paper tackles point cloud registration by proposing DeepCLR, an end-to-end deep architecture that predicts alignment without explicit correspondences, achieving state-of-the-art accuracy and the lowest run-time on KITTI odometry and ModelNet40 datasets.
This work addresses the problem of point cloud registration using deep neural networks. We propose an approach to predict the alignment between two point clouds with overlapping data content, but displaced origins. Such point clouds originate, for example, from consecutive measurements of a LiDAR mounted on a moving platform. The main difficulty in deep registration of raw point clouds is the fusion of template and source point cloud. Our proposed architecture applies flow embedding to tackle this problem, which generates features that describe the motion of each template point. These features are then used to predict the alignment in an end-to-end fashion without extracting explicit point correspondences between both input clouds. We rely on the KITTI odometry and ModelNet40 datasets for evaluating our method on various point distributions. Our approach achieves state-of-the-art accuracy and the lowest run-time of the compared methods.