Image guided reconstruction of un-sampled data: a filling technique for cultural heritage models

Dellepiane M., Venturi A., Scopigno R.

Hole filling  Image registration  Artificial Intelligence  Computer Vision and Pattern Recognition  Laser triangulation  Software  I.3.7 Three-Dimensional Graphics and Realism 

Cultural Heritage (CH) is one of the major fields of application of 3D scanning technologies. In this context, one of the main limitations perceived by the practitioners is the uncompleteness of the sampling. Whenever we scan a complex artifact, the produced sampling usually presents a large number of unsampled regions. Many algorithmic solutions exist to close those gaps (from specific hole-filling algorithms to the drastic solution of using water-tight reconstruction methods). Unfortunately, adding patches over un-sampled regions is an issue in CH applications: if the 3D model should be used as a master document over the shape (and status) of the artwork, informed CH curators usually do not accept that an algorithm is used to {em guess} portions of a surface. In this paper, we present a low-cost setup and related algorithms to reconstruct un-sampled portions of the 3D models by inferring information about the real shape of the missing region from photographs. Data needed to drive the surface completion process are obtained by coupling a calibrated pattern of laser diodes to a digital camera. Thus, we are proposing a simple active acquisition device (based on consumer components and more flexible than standard 3D scanning devices) to improve selectively the sampling produced by a standard 3D scanning device. After acquiring one or more images with the laser-enhanced camera, an almost completely automatic process analyzes the image/s in order to extract the pattern, to estimate the laser projector intersections over the surface and determining coordinates of those points (using the consolidated triangulation approach). Then, the gathered geometric data are used to steer the hole filling in order to obtain a patch which is coherent with the real shape of the object. A series of tests on real objects proves that our method is able to recover geometrical features that cannot be reconstructed using state-of-the-art methods. Consequently, it can be used to obtain complete 3D models without creating plausible but false data.

Source: International Journal of Computer Vision 94 (2011): 2–11. doi:10.1007/s11263-010-0382-2

Publisher: Springer, Netherlands, Paesi Bassi

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