Scale robust point matching‐Net: End‐to‐end scale point matching using Lie group. Issue 7 (13th August 2022)

• Record Type: Journal Article
• Authors: Wang, Xin; Ding, Hui; Zhao, Guangwei; Peng, Yaxin; Shen, Chaomin
• Other Names: Geo Yulan guestEditor.; Wang Hanyun guestEditor.; Clark Ronald guestEditor.; Berrett Stefano guestEditor.; Bennamoun Mohammed guestEditor.
• Is Part Of: IET computer vision. Volume 16:Issue 7(2022)
• Page Start: 655
• Page End: 666
• Publication Date: 2022-08-13
• Subjects: Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/cvi2.12134
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract: Point cloud matching is an important procedure in a variety of computer vision tasks. Traditional point cloud matching methods have made great progress, while neural network‐based approaches are becoming a trend, powered by their strong capabilities of feature extraction. Existing point matching neural networks, however, mainly focus on the rigid transformation. More complex transformations should also be considered in many scenarios. In this regard, the authors extend the rigid registration to non‐rigid cases and propose a network called the Scale Robust Point Matching (SRPM)‐Net for scale point matching. This robust structure‐preserving network is implemented by incorporating Lie group parametrisation. It is conducted by Lie group linearisation representation with the constraints of parameters under the corresponding basis of Lie algebra. SRPM‐Net preserves the structure of the solution and avoids degeneration. The contributions of this paper lie in two aspects: Most importantly, SRPM‐Net provides an extendable framework for handling complicated transformations. Secondly, it introduces a new feature learning module, which better preserves the shape structure by aggregating the high‐dimensional feature and calculating the normal vector of point cloud surface automatically. Experimental results show that SRPM‐Net is more robust and accurate than existing traditional and recent deep learning methods under various situations.