Glira, P.; Weidinger, C.; Otepka-Schremmer, J.; Ressl, C.; Pfeifer, N.; Haberler-Weber, M. Nonrigid Point Cloud Registration Using Piecewise Tricubic Polynomials as Transformation Model. Remote Sens.2023, 15, 5348.
Glira, P.; Weidinger, C.; Otepka-Schremmer, J.; Ressl, C.; Pfeifer, N.; Haberler-Weber, M. Nonrigid Point Cloud Registration Using Piecewise Tricubic Polynomials as Transformation Model. Remote Sens. 2023, 15, 5348.
Glira, P.; Weidinger, C.; Otepka-Schremmer, J.; Ressl, C.; Pfeifer, N.; Haberler-Weber, M. Nonrigid Point Cloud Registration Using Piecewise Tricubic Polynomials as Transformation Model. Remote Sens.2023, 15, 5348.
Glira, P.; Weidinger, C.; Otepka-Schremmer, J.; Ressl, C.; Pfeifer, N.; Haberler-Weber, M. Nonrigid Point Cloud Registration Using Piecewise Tricubic Polynomials as Transformation Model. Remote Sens. 2023, 15, 5348.
Abstract
Non-rigid registration presents a significant challenge in the domain of point cloud processing. The general objective is to model complex non-rigid deformations between two or more overlapping point clouds. Applications are diverse and span multiple research fields, including registration of topographic data, scene flow estimation, and dynamic shape reconstruction. To provide context, we begin with a general introduction to the topic of point cloud registration, including a categorization of methods. Next, we introduce a general mathematical formulation for point cloud registration and extend it to address non-rigid registration. A detailed discussion and categorization of existing approaches to non-rigid registration follows. We then introduce our own method where the usage of piece-wise tricubic polynomials for modeling non-rigid deformations is proposed. Our method offers several advantages over existing methods. These advantages include easy control of flexibility through a small number of intuitive tuning parameters, a closed-form optimization solution, and an efficient transformation of huge point clouds. We demonstrate our method through multiple examples that cover a broad range of applications, with a focus on remote sensing applications - namely, the registration of Airborne Laser Scanning (ALS), Mobile Laser Scanning (MLS), and Terrestrial Laser Scanning (TLS) point clouds. The implementation of our algorithms is open source and can be found on GitHub.
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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