Reflectance Gradients for Material Recognition

Kristin Dana
Rutgers University

We introduce a novel method for using reflectance to identify materials. Reflectance offers a unique signature of a material but is challenging to measure and use for recognizing materials due to its high-dimensionality. Cameras can be used to capture reflectance, but typically from a sparse set of viewing angles. In this work, one-shot reflectance is captured using a unique optical camera measuring dense reflectance disks where the pixel coordinates correspond to surface viewing angles. Angular gradients computed in this reflectance space reveal the material class. These reflectance disks encode discriminative information for efficient and accurate material recognition. We introduce a framework called reflectance hashing that models the reflectance disks with dictionary learning and binary hashing. We demonstrate the effectiveness of reflectance hashing for material recognition with a number of real-world materials.

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