Savelonas, Michalis A.Andreadis, AnthousisPapaioannou, GeorgiosMavridis, PavlosTobias Schreck and Tim Weyrich and Robert Sablatnig and Benjamin Stular2017-09-272017-09-272017978-3-03868-037-62312-6124https://doi.org/10.2312/gch.20171305https://diglib.eg.org:443/handle/10.2312/gch20171305Virtual reassembly problems are often encountered in the cultural heritage domain. The reassembly or "puzzling" problem is typically described as the process for the identification of corresponding pieces within a part collection, followed by the clustering and pose estimation of multiple parts that result in a virtual representation of assembled objects. This work addresses this problem with an efficient, user-guided computational approach. The proposed approach augments the typical reassembly pipeline with a smart culling step, where geometrically incompatible fragment combinations can be quickly rejected. After splitting each fragment into potentially fractured and intact facets, each intact facet is examined for prominent linear or curved structures and a heuristic test is employed to evaluate the plausibility of facet pairs, by comparing the number of feature curves associated with each facet, as well as the geometric texture of associated intact surfaces. This test excludes many pairwise combinations from the remaining part of the reassembly process, significantly reducing overall time cost. For all facet pairs that pass the initial plausibility test, pairwise registration driven by enhanced simulated annealing is applied, followed by multipart registration. The proposed reassembly approach is evaluated on real scanned data and our experiments demonstrate an increase in efficiency that ranges from 30% to more than 500% in some cases, depending on the number of culled combinations.Computing methodologiesShape analysisApplied computingArts and humanities10.2312/gch.20171305137-144