从单张照片自动推断具有解剖学意义的实木纹理
Automatic inference of a anatomically meaningful solid wood texture from a single photograph
AI总结：
针对现有木材外观模型无法自动匹配样本、难重建解剖学体积信息的问题，提出基于弯曲Gabor滤波器的年轮检测初始化方案与相位损失函数，实现实木纹理的自动推断，适用于多类木材。
摘要：
木材是一种外观色域极广的体积材料，众多的表面处理工艺进一步拓展了其外观多样性。计算机图形学在创建复杂灵活的外观模型方面已取得长足进展，能够生成逼真的木材材质渲染效果。然而，这些模型尚无法实现与具体木材样本的全自动外观匹配，必须手动进行微调。更通用的外观匹配策略无法重建具有解剖学意义的体积信息，这对于木材内部结构至关重要的应用场景不可或缺，例如由实木块加工而成的非平面家具部件、薄木层的半透明外观，或是树木年代学领域。本文提出了实现程序化木材外观模型与样本照片自动匹配的两个关键要素：基于年轮结构检测与建模的优质初始化方案，以及基于相位的损失函数——该函数可精确恢复生长轮形变，并生成具有解剖学意义的结果。我们的年轮检测技术基于弯曲Gabor滤波器构建，可在相当广泛的木材类型中稳定工作。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
