Academy & Industry Research Collaboration Center (AIRCC)

Volume 12, Number 05, March 2022

BTF Prediction Model using Unsupervised Learning


Soichiro Kimura1, Kensuke Tobitani2 and Noriko Nagata1, 1Kwansei Gakuin University, Japan, 2University of Nagasaki, Japan


The impressions evoked by textures are called affective textures, and are considered to be important in evaluating and judging the quality of an object. And, technologies for understanding and controlling sensory textures are needed in product design. In this study, we propose a BTF prediction method using DNN as a first attempt to generate textures based on affective texture recognition. The method uses a series of continuously varying viewpoint angles of a texture image as the input signal. This method enables the generation of texture images with continuously changing angles. We tested the validity of the proposed method by using textile, wood and paper. The results show that the proposed method is effective for predicting diffuse reflection optical properties and irregular and regular patterns.


PredNet, Machine Learning, BTF, Affective texture.