When light interacts with surfaces and participating media, it is altered in terms of its spectrum, polarization state, and spatial and angular distributions. Modeling and analyzing these processes has a long history in vision, and it has deepened our understanding of biological vision systems and enabled the development of a variety of computational tools for analyzing and organizing visual data. Over the last decade, with the acceleration of digital photography and the advances in appearance scanners, image sensors, and displays, we have seen explosive growth in the amount of visual data that is available, and equally explosive growth in the opportunities for image understanding by machines. This workshop will leverage this growth and exploit these opportunities by providing new insights for the understanding of color and photometry in computer vision. As color and photometry are shared among various research fields, this workshop places them at the junctions of different areas, including color science, applied optics, computational photography, computer vision, computer graphics, and machine learning. It seeks to enable knowledge discovery using area-specific expertise and cross-understanding. We encourage researchers to formulate innovative color theories, color representations, and color processing techniques, and to evaluate their effectiveness. We also encourage new theories and processes for organizing images and inferring scene information from images through analysis of photometry and/or color that is motivated by perception, physics, and phenomenology. We are soliciting original contributions that address a wide range of theoretical and practical issues including, but not limited to: Theory: Color spaces; reflection models; scattering models; light transport; multi-spectral, hyper-spectral, and polarization models; appearance analysis; color appearance models. Sensors: Imaging systems; active illumination systems; spectrum and polarization sensing; light probes; shape and material scanners; radiometric and colorimetric calibration. Image/Video Processing: Filtering, enhancement, feature detection, and segmentation informed by color and/or photometry; white balance; relighting; image decomposition via intrinsic images, specularity removal, and shadow removal; color texture; colorization. Material, Object, Scene, and Video Recognition: Photometric invariants; color invariants; material recognition; lighting estimation; shape estimation; color saliency; color constancy; color descriptors and matching. Vision Science: Material perception; shape perception; lighting perception; lightness and color perception. Applications: Industrial inspection; human computer interaction; navigation; medical diagnosis; biology and biomedicine.