학술논문
Learning-Based Inverse Bi-Scale Material Fitting From Tabular BRDFs
Document Type
Periodical
Author
Source
IEEE Transactions on Visualization and Computer Graphics IEEE Trans. Visual. Comput. Graphics Visualization and Computer Graphics, IEEE Transactions on. 28(4):1810-1823 Apr, 2022
Subject
Language
ISSN
1077-2626
1941-0506
2160-9306
1941-0506
2160-9306
Abstract
Relating small-scale structures to large-scale appearance is a key element in material appearance design. Bi-scale material design requires finding small-scale structures – meso-scale geometry and micro-scale BRDFs – that produce a desired large-scale appearance expressed as a macro-scale BRDF. The adjustment of small-scale geometry and reflectances to achieve a desired appearance can become a tedious trial-and-error process. We present a learning-based solution to fit a target macro-scale BRDF with a combination of a meso-scale geometry and micro-scale BRDF. We confront challenges in representation at both scales. At the large scale we need macro-scale BRDFs that are both compact and expressive. At the small scale we need diverse combinations of geometric patterns and potentially spatially varying micro-BRDFs. For large-scale macro-BRDFs, we propose a novel 2D subset of a tabular BRDF representation that well preserves important appearance features for learning. For small-scale details, we represent geometries and BRDFs in different categories with different physical parameters to define multiple independent continuous search spaces. To build the mapping between large-scale macro-BRDFs and small-scale details, we propose an end-to-end model that takes the subset BRDF as input and performs classification and parameter estimation on small-scale details to find an accurate reconstruction. Compared with other fitting methods, our learning-based solution provides higher reconstruction accuracy and covers a wider gamut of appearance.