We present the Mokume dataset for solid wood texturing consisting of 190 cube-shaped samples of various hard and softwood species documented by high-resolution exterior photographs, annual ring annotations, and volumetric computed tomography (CT) scans. A subset of samples further includes photographs along slanted cuts through the cube for validation purposes. Using this dataset, we propose a three-stage inverse modeling pipeline to infer solid wood textures using only exterior photographs. Our method begins by evaluating a neural model to localize year rings on the cube face photographs. We then extend these exterior 2D observations into a globally consistent 3D representation by optimizing a procedural growth field using a novel iso-contour loss. Finally, we synthesize a detailed volumetric color texture from the growth field. For this last step, we propose two methods with different efficiency and quality characteristics: a fast inverse procedural texture method, and a neural cellular automaton (NCA). We demonstrate the synergy between the Mokume dataset and the proposed algorithms through comprehensive comparisons with unseen captured data. We also present experiments demonstrating the efficiency of our pipeline's components against ablations and baselines. Our code, the dataset, and reconstructions are available via https://mokumeproject.github.io/.

We present a procedural framework for modeling the annual ring pattern of solid wood with knots. Although wood texturing is a well-studied topic, there have been few previous attempts at modeling knots inside the wood texture. Our method takes the skeletal structure of a tree log as input and produces a three-dimensional scalar field representing the time of added growth, which defines the volumetric annual ring pattern. First, separate fields are computed around each strand of the skeleton, i.e., the stem and each knot. The strands are then merged into a single field using smooth minimums. We further suggest techniques for controlling the smooth minimum to adjust the balance of smoothness and reproduce the distortion effects observed around dead knots. Our method is implemented as a shader program running on a GPU with computation times of approximately 0.5 s per image and an input data size of 600 KB. We present rendered images of solid wood from pine and spruce as well as plywood and cross-laminated timber (CLT). Our results were evaluated by wood experts, who confirmed the plausibility of the rendered annual ring patterns. Link to code: https://github.com/marialarsson/procedural_knots.