Learned Inference of Annual Ring Pattern of Solid Wood

We propose a method for inferring the internal anisotropic volumetric texture of a given wood block from annotated photographs of its external surfaces. The global structure of the annual ring pattern is represented using a continuous spatial scalar field referred to as the growth time field (GTF). First, we train a generic neural model that can represent various GTFs using procedurally generated training data. Next, we fit the generic model to the GTF of a given wood block based on surface annotations. Finally, we convert the GTF to an annual ring field (ARF) revealing the layered pattern and apply neural style transfer to render orientation‐dependent small‐scale features and colors on a cut surface. We show rendered results of various physically cut real wood samples. Our method has physical and virtual applications such as cut‐preview before subtractive fabricating solid wood artifacts and simulating object breaking. We propose a learning‐based method for inferring the internal anisotropic volumetric texture of a given wood block from annotated photographs of its external surfaces. The method has physical and virtual applications such as cut‐preview before subtractive fabricating solid wood artifacts and simulating object breaking.