To evaluate a car’s design before its introduction to the market, automobile companies traditionally conduct customer surveys using clay models or real products. Recently, however, virtual reality (VR) has attracted significant attention as an efficient alternative to this costly and time-consuming practice. However, from the viewpoint of a customer’s intention to purchase, based on the design of the car, the differences between evaluations made using VR and by viewing an actual vehicle need to be clarified. In this study, the perception difference between actual space, VR space, and paper was verified with a randomized controlled trial involving four groups: (1) an actual car group, (2) a photographed VR group, (3) a computer-generated VR (CG VR) group, and (4) a paper group. By preparing the least expensive (paper) group, the usefulness of VR could be further clarified. The results showed that, whereas there is no perception difference for purchase intention and style attributes, there is a perception difference in terms of the factors underlying the purchase intention. The best way to convey the comfort of the interior space was CG VR, given that the size can be defined by data. Therefore, this was slightly advantageous over photograph-based VR, for which the interior size could not be defined. In addition, it became clear that it was very difficult to convey the amount of interior space using the paper-based method. These findings can help overcome the constraints of current design surveys and contribute to customer-oriented manufacturing.

Verification of perception difference between actual space and VR space in car design

Learning color mixing is difficult for novice painters. In order to support novice painters in learning color mixing, we propose a prediction model for semitransparent pigment mixtures and use its prediction results to create a Smart Palette system. Such a system is constructed by first building a watercolor dataset with two types of color mixing data, indicated by transmittance and reflectance: incrementation of the same primary pigment and a mixture of two different pigments. Next, we apply the collected data to a deep neural network to train a model for predicting the results of semitransparent pigment mixtures. Finally, we constructed a Smart Palette that provides easily-followable instructions on mixing a target color with two primary pigments in real life: when users pick a pixel, an RGB color, from an image, the system returns its mixing recipe which indicates the two primary pigments being used and their quantities.

Prediction Model for Semitransparent Watercolor Pigment Mixtures Using Deep Learning with a Dataset of Transmittance and Reflectance.

Modeling and Estimation for Surface-Spectral Reflectance of Watercolor Paintings

A Study on Color and Brightness Perception in Virtual Reality Space

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Modeling and Estimation for Surface-Spectral Reflectance of Watercolor Paintings

A Study on Color and Brightness Perception in Virtual Reality Space