Global non-rigid alignment of 3-D scans

Abstract

Figure 1: Using non-rigid global alignment techniques, we are able to preserve more surface detail with fewer merging artifacts, as seen in closeups of the upper-left edge of this Forma Urbis Romae fragment. (c) Using non-rigid alignment (section 3.3) improves alignment enough to eliminate most surface noise. (d) Incorporating locally weighted ICP-based feature correspondences (section 3.2) further improves detail alignment, notably eliminating double edges. AbstractA key challenge in reconstructing high-quality 3D scans is registering data from different viewpoints. Existing global (multiview) alignment algorithms are restricted to rigid-body transformations, and cannot adequately handle non-rigid warps frequently present in real-world datasets. Moreover, algorithms that can compensate for such warps between pairs of scans do not easily generalize to the multiview case. 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. The process first obtains sparse correspondences between views using a locally weighted, stability-guaranteeing variant of iterative closest points (ICP). Global positions for feature points are found using a relaxation method, and the scans are warped to their final positions using thin-plate splines. Our framework efficiently handles large datasets -thousands of scans comprising hundreds of millions of samples -for both rigid and non-rigid alignment, with the nonrigid case requiring little overhead beyond rigid-body alignment. 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. *

Parallel Poisson Surface Reconstruction

“…The authors would also like to express particular thanks to Szymon Rusinkiewicz and Benedict Brown for providing non-rigid body aligned Lucy and David scan data [22]. This work was supported in part by an NSF CAREER Award.…”

Scan integration as a labelling problem

“…This scheme works because registration errors are not evenly distributed across points. Global registration [42,38,43,44,45] is often used with the aim of evenly distributing registration errors between scans, but within a single scan, different points suffer from different registration errors.…”

Non-rigid Shape Matching Using Geometry and Photometry

“…Further in [7], the authors propose a non rigid registration method by piecewise rigid registration and local thin plate spline alignment of range scan images. Elad et al [8] take an intrinsic point of view of the matching problem.…”