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This paper proposes a deep learning model for point cloud registrations of different sizes. 3D point clouds play a very important role in various fields. They have been widely studied, and recently deep learning has also started to deal with point clouds. PointNet was the first deep learning model for point cloud classification and semantic segmentation. Since then, methods based on PointNet for tasks like point cloud registration have also been proposed. However, these methods are only suitable for identical or nearly identical point clouds. However, in practice, the sizes of point clouds vary depending on the capture distance, sensor type, the environment, and many other factors. Therefore, it is often the case that point clouds that need to be registered are of very different sizes. For example, point clouds captured in the same environment by an omnidirectional LiDAR and an RGB-D camera will have very different sizes. Conventional methods cannot cope with such situations. In this paper, we propose ‘PointpartNet', a new deep neural network based on partial feature extraction. This network enables feature extraction of partial point clouds by partitioning the point clouds. It uses the features of partial point clouds to search for matching regions between point clouds of different sizes. This makes it capable of registering point clouds of different sizes. In qualitative experiments, we demonstrate its high robustness and accuracy for point cloud registration of different sizes in comparison to previous research.

PointpartNet: 3D point-cloud registration via deep part-based feature extraction