R-PointHop: A Green, Accurate, and Unsupervised Point Cloud Registration Method

• Published in: IEEE transactions on image processing 2022, Vol.31, p.2710-2725
• Main Authors: Kadam, Pranav, Zhang, Min, Liu, Shan, Kuo, C. -C. Jay
• Format: Article
• Language: English
• Subjects: 3D feature descriptor; Deep learning; Feature extraction; local reference frame (LRF); Machine learning; Point cloud compression; Point cloud registration; Principal component analysis; Registration; Rotation; rotation invariance; Task analysis; Three dimensional models; Three-dimensional displays; Transforms
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2022.3160609

Inspired by the recent PointHop classification method, an unsupervised 3D point cloud registration method, called R-PointHop, is proposed in this work. R-PointHop first determines a local reference frame (LRF) for every point using its nearest neighbors and finds local attributes. Next, R-PointHop obtains local-to-global hierarchical features by point downsampling, neighborhood expansion, attribute construction and dimensionality reduction steps. Thus, point correspondences are built in hierarchical feature space using the nearest neighbor rule. Afterwards, a subset of salient points with good correspondence is selected to estimate the 3D transformation. The use of the LRF allows for invariance of the hierarchical features of points with respect to rotation and translation, thus making R-PointHop more robust at building point correspondence, even when the rotation angles are large. Experiments are conducted on the 3DMatch, ModelNet40, and Stanford Bunny datasets, which demonstrate the effectiveness of R-PointHop for 3D point cloud registration. R-PointHop's model size and training time are an order of magnitude smaller than those of deep learning methods, and its registration errors are smaller, making it a green and accurate solution. Our codes are available on GitHub ( https://github.com/pranavkdm/R-PointHop ).