Automatic inference of a anatomically meaningful solid wood texture from a single photograph

TL;DR

This paper introduces an automatic method to infer a volumetric, anatomically meaningful wood texture from a single photograph, enabling realistic rendering and analysis of wood's internal structure.

Contribution

It presents a novel approach combining ring detection with a phase-based loss for automatic, anatomically accurate wood appearance modeling from a single image.

Findings

Effective ring detection across various wood types

Accurate reconstruction of growth ring deformations

Enables realistic rendering and internal structure analysis

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.