Material Classification using BRDF Slices

Segmenting images into distinct material types is a very useful capability. Most work in image segmentation addresses the case where only a single image is available. Some methods improve on this by collecting HDR or multispectral images. However, it is also possible to use the reflectance properties of the materials to obtain better results. By acquiring many images of an object under different lighting conditions we have more samples of the surfaces Bidirectional Reflectance Distribution Function (BRDF). We show that this additional information enlarges the class of material types that can be well separated by segmentation, and that properly treating the information as samples of the BRDF further increases accuracy without requiring an explicit estimation of the material BRDF.

• Type: Conference
• Year: 2009
• Where: CVPR
• Authors: Oliver Wang (University of California, Santa Cruz), Prabath Gunawardane (University of California, Santa Cruz), Steven Scher (University of California, Santa Cruz), James Davis (University of California, Santa Cruz)
• Keywords: BRDF Slices, Computer Vision, CVPR 2009, Material Classification