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. *
Figure 1: Our acquisition system, deployed at the Akrotiri Excavation, Thera. We use a flatbed scanner to capture high-resolution images and normals of wall painting fragments (shown at left), and multiple 3-D scanners to acquire geometry. A single user can operate up to four scanners simultaneously, while a second user operates the flatbed scanner and verifies processing results. This yields a throughput of approximately 10 fragments per hour. Our matching algorithm correctly finds the only two matches in this data set using the scanned 3-D geometry. AbstractAlthough mature technologies exist for acquiring images, geometry, and normals of small objects, they remain cumbersome and time-consuming for non-experts to employ on a large scale. In an archaeological setting, a practical acquisition system for routine use on every artifact and fragment would open new possibilities for archiving, analysis, and dissemination. We present an inexpensive system for acquiring all three types of information, and associated metadata, for small objects such as fragments of wall paintings. The acquisition system requires minimal supervision, so that a single, non-expert user can scan at least 10 fragments per hour. To achieve this performance, we introduce new algorithms to robustly and automatically align range scans, register 2-D scans to 3-D geometry, and compute normals from 2-D scans. As an illustrative application, we present a novel 3-D matching algorithm that efficiently searches for matching fragments using the scanned geometry.
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