Authors
Steve Marschner
Cornell University
Portals
Summary
Work on three such inverse rendering problems is described. The first, inverse lighting, assumes knowledge of geometry, reflectance, and the recorded photograph and solves for the lighting in the scene. The second two inverse rendering problems solve for unknown reflectance, given images with known geometry, lighting, and camera positions including photographic texture measurement and image-based BRDF measurement.
Abstract
Creating realistic images has been a major focus in the study of computer graphics for much of its history. This effort has led to mathematical models and algorithms that can compute predictive, or physically realistic, images from known camera positions and scene descriptions that include the geometry of objects, the reflectance of surfaces, and the lighting used to illuminate the scene. These images accurately describe the physical quantities that would be measured from a real scene. Because these algorithms can predict real images, they can also be used in inverse problems to work backward from photographs to attributes of the scene. Work on three such inverse rendering problems is described. The first, inverse lighting, assumes knowledge of geometry, reflectance, and the recorded photograph and solves for the lighting in the scene. A technique using a linear least-squares system is proposed and demonstrated. Also demonstrated is an application of inverse lighting, called re-lighting, which modifies lighting in photographs. The second two inverse rendering problems solve for unknown reflectance, given images with known geometry, lighting, and camera positions. Photographic texture measurement concentrates on capturing the spatial variation in an object's reflectance. The resulting system begins with scanned 3D models of real objects and uses photographs to construct accurate, high-resolution textures suitable for physically realistic rendering. The system is demonstrated on two complex natural objects with detailed surface textures. Image-based BRDF measurement takes the opposite approach to reflectance measurement, capturing the directional characteristics of a surface's reflectance by measuring the bidirectional reflectance distribution function, or BRDF. Using photographs of an object with spatially uniform reflectance, the BRDFs of paints and papers are measured with completeness and accuracy that rival that of measurements obtained using specialized devices. The image-based approach and novel light source positioning technique require only general-purpose equipment, so the cost of the apparatus is low compared to conventional approaches. In addition, very densely sampled data can be measured very quickly, when the wavelength spectrum of the BRDF does not need to be measured in detail.