Automatic archaeological fragment reconstruction using hierarchical depth maps

Vidal, Eduardo Vendrell; Belenguer, Carlos Sánchez

doi:10.1109/clei.2012.6427149

Cited by 4 publications

(1 citation statement)

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“…[YLL12] employ a multi‐scale descriptor and conduct heat kernel‐based partial matching between break boundaries, while Vendrell et al . [VVSB12] use hierarchical depth maps to ensure global correction of the reconstruction and to evaluate the correspondence between fragments using an optimistic estimator. Brown et al .…”

Section: Micro‐geometrymentioning

confidence: 99%

A Survey of Geometric Analysis in Cultural Heritage

Pintus

Pal

Yang

et al. 2015

Computer Graphics Forum

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We present a review of recent techniques for performing geometric analysis in cultural heritage applications. The survey is aimed at researchers in the areas of computer graphics, computer vision, and cultural heritage computing, as well as to scholars and practitioners in the cultural heritage field. The problems considered include shape perception enhancement, restoration and preservation support, monitoring over time, object interpretation, and collection analysis. All of these problems typically rely on an understanding of the structure of the shapes in question at both a local and global level. In this survey, we discuss the different problem forms and review the main solution methods, aided by classification criteria based on the geometric scale at which the analysis is performed and the cardinality of the relationships among object parts exploited during the analysis. We finalize the report by discussing open problems and future perspectives.

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Section: Micro‐geometrymentioning

confidence: 99%

A Survey of Geometric Analysis in Cultural Heritage

Pintus

Pal

Yang

et al. 2015

Computer Graphics Forum

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Wall Painting Reconstruction Using a Genetic Algorithm

Sizikova

2017

J. Comput. Cult. Herit.

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Global reconstruction of two-dimensional wall paintings (frescoes) from fragments is an important problem for many archaeological sites. The goal is to find the global position and rotation for each fragment so that all fragments jointly “reconstruct” the original surface (i.e., solve the puzzle). Manual fragment placement is difficult and time-consuming, especially when fragments are irregularly shaped and uncolored. Systems have been proposed to first acquire 3D surface scans of the fragments and then use computer algorithms to solve the reconstruction problem. These systems work well for small test cases and for puzzles with distinctive features, but fail for larger reconstructions of real wall paintings with eroded and missing fragments due to the complexity of the reconstruction search space. We address the search problem with an unsupervised genetic algorithm: we evolve a pool of partial reconstructions that grow through recombination and selection over the course of generations. We introduce a novel algorithm for combining partial reconstructions that is robust to noise and outliers, and we provide a new selection procedure that balances fitness and diversity in the population. In experiments with a benchmark dataset, our algorithm is able to achieve larger and more accurate global reconstructions than previous automatic algorithms.

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