Automatic inference of a anatomically meaningful solid wood texture from a single photograph (2302.01820v1)

Abstract: Wood is a volumetric material with a very large appearance gamut that is further enlarged by numerous finishing techniques. Computer graphics has made considerable progress in creating sophisticated and flexible appearance models that allow convincing renderings of wooden materials. However, these do not yet allow fully automatic appearance matching to a concrete exemplar piece of wood, and have to be fine-tuned by hand. More general appearance matching strategies are incapable of reconstructing anatomically meaningful volumetric information. This is essential for applications where the internal structure of wood is significant, such as non-planar furniture parts machined from a solid block of wood, translucent appearance of thin wooden layers, or in the field of dendrochronology. In this paper, we provide the two key ingredients for automatic matching of a procedural wood appearance model to exemplar photographs: a good initialization, built on detecting and modelling the ring structure, and a phase-based loss function that allows to accurately recover growth ring deformations and gives anatomically meaningful results. Our ring-detection technique is based on curved Gabor filters, and robustly works for a considerable range of wood types.