基于无监督学习的BTF预测模型

Soichiro Kimura, Kensuke Tobitani, N. Nagata
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引用次数: 0

摘要

由纹理引起的印象被称为情感纹理,被认为是评价和判断一个物体质量的重要因素。同时,在产品设计中也需要理解和控制感官肌理的技术。在本研究中,我们提出了一种基于深度神经网络的BTF预测方法,作为基于情感纹理识别生成纹理的第一次尝试。该方法使用纹理图像的一系列连续变化的视点角度作为输入信号。该方法可以生成角度连续变化的纹理图像。我们用纺织品、木材和纸张测试了所提出方法的有效性。结果表明,该方法可以有效地预测漫反射光学性质以及不规则和规则图案。
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BTF Prediction Model using Unsupervised Learning
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.
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