Perceptual Dimensions of Wood Materials

Materials exhibit an extraordinary range of visual appearances. Characterising and quantifying appearance is important not only for basic research on perceptual mechanisms, but also for computer graphics and a wide range of industrial applications. While methods exist for capturing and representing the optical properties of materials and how they vary across surfaces (Haindl & Filip., 2013), the representations are typically very high-dimensional, and how these representations relate to subjective perceptual impressions of material appearance remains poorly understood. Here, we used a data-driven approach to characterising the perceived appearance characteristics of 30 samples of wood veneer using a ‘visual fingerprint’ that describes each sample as a multidimensional feature vector, with each dimension capturing a different aspect of the appearance. Fifty-six crowd-sourced participants viewed triplets of movies depicting different wood samples as the sample rotated. Their task was to report which of the two match samples was subjectively most similar to the test sample. In another online experiment 45 participants rated ten wood-related appearance characteristics for each of the samples. The results reveal a consistent embedding of the samples across both experiments and a set of 9 perceptual dimensions capturing aspects including the roughness, directionality and spatial scale of the surface patterns. We also showed that a weighted linear combination of eleven image statistics, inspired by the rating characteristics, predicts perceptual dimensions well.

材料展现出极为丰富的视觉外观。对外观进行特征描述和量化不仅对感知机制的基础研究至关重要，而且对于计算机图形学以及广泛的工业应用领域亦然。尽管存在捕捉和表征材料光学性质及其表面变化的方法（Haindl & Filip., 2013），但这些表征通常维度极高，而这些表征与材料外观的主观感知印象之间的关系尚缺乏深入理解。在本研究中，我们采用数据驱动的方法，对30种木皮样本的感知外观特征进行描述，采用‘视觉指纹’将每个样本描述为一个多维特征向量，其中每个维度捕捉外观的不同方面。56名来自众包的参与者观看了不同木皮样本的三重电影，样本在旋转过程中展示。他们的任务是报告哪两个匹配样本在主观上与测试样本最为相似。在另一项在线实验中，45名参与者对每个样本的十个与木材相关的外观特征进行了评级。结果表明，两种实验中样本的嵌入均保持一致，并揭示了一套包含9个感知维度，这些维度捕捉了包括表面纹理的粗糙度、方向性和空间尺度在内的各个方面。我们还展示了受评级特征启发的11个图像统计量的加权线性组合，能够很好地预测感知维度。