Hiroki Okawa

Papers

Total Citations

H-Index

About

Hiroki Okawa is a computer vision researcher whose work focuses on the visual recognition of material properties, particularly surface wetness and color. His major contributions lie in developing methods to estimate wetness and material appearance from a single multispectral image—a challenging problem with real-world implications for autonomous vehicles, robotics, and quality inspection. In his most cited work, "Wetness and Color from a Single Multispectral Image" (2017, 9 citations), Okawa demonstrated how to computationally separate the effects of water on surface appearance, enabling systems to detect slippery roads, muddy trails, or fresh produce. He extended this in "Estimation of Wetness and Color from a Single Multispectral Image" (2019, 6 citations), refining the approach to handle more complex lighting and surface conditions. By addressing the fundamental physics of how water alters reflectance, Okawa’s research bridges low-level vision and practical safety applications. His work is notable for tackling an underexplored but critical visual cue—surface wetness—and for providing a foundation for future work in material recognition and autonomous navigation.