![non rigid alignment artec studio non rigid alignment artec studio](https://i.ytimg.com/vi/epcGdGSP6Uw/hqdefault.jpg)
Global positions for feature points are found using a relaxation method, and the scans are warped to their final positions using thin-plate splines. The process first obtains sparse correspondences between views using a locally weighted, stability-guaranteeing variant of iterative closest points (ICP). We present an algorithm for obtaining a globally optimal alignment of multiple overlapping datasets in the presence of low-frequency non-rigid deformations, such as those caused by device nonlinearities or calibration error. Moreover, algorithms that can compensate for such warps between pairs of scans do not easily generalize to the multiview case. Existing global (multiview) alignment algorithms are restricted to rigid-body transformations, and cannot adequately handle non-rigid warps frequently present in real-world datasets. We demonstrate that, relative to rigid-body registration, it improves the quality of alignment and better preserves detail in 3D datasets from a variety of scanners exhibiting non-rigid distortion.Ībstract = "A key challenge in reconstructing high-quality 3D scans is registering data from different viewpoints. Our framework efficiently handles large datasets -thousands of scans comprising hundreds of millions of samples -for both rigid and non-rigid alignment, with the non-rigid case requiring little overhead beyond rigid-body alignment.
![non rigid alignment artec studio non rigid alignment artec studio](https://dl.acm.org/cms/attachment/721696d7-2f22-45aa-b305-9040ad73ebb4/cvmp19-13-fig1.jpg)
A key challenge in reconstructing high-quality 3D scans is registering data from different viewpoints.