Radiometric Scene Decomposition: Scene Reflectance, Illumination, and Geometry from RGB-D Images
This addresses a long-standing challenge in computer vision for applications like scene understanding, but it is incremental as it builds on existing RGB-D data and radiometric modeling.
The authors tackled the problem of recovering radiometric properties (reflectance, illumination, geometry) from real-world scenes using RGB-D images, and their method successfully handled multiple objects with complex materials and interactions, as demonstrated on synthetic and real-world scenes.
Recovering the radiometric properties of a scene (i.e., the reflectance, illumination, and geometry) is a long-sought ability of computer vision that can provide invaluable information for a wide range of applications. Deciphering the radiometric ingredients from the appearance of a real-world scene, as opposed to a single isolated object, is particularly challenging as it generally consists of various objects with different material compositions exhibiting complex reflectance and light interactions that are also part of the illumination. We introduce the first method for radiometric scene decomposition that handles those intricacies. We use RGB-D images to bootstrap geometry recovery and simultaneously recover the complex reflectance and natural illumination while refining the noisy initial geometry and segmenting the scene into different material regions. Most important, we handle real-world scenes consisting of multiple objects of unknown materials, which necessitates the modeling of spatially-varying complex reflectance, natural illumination, texture, interreflection and shadows. We systematically evaluate the effectiveness of our method on synthetic scenes and demonstrate its application to real-world scenes. The results show that rich radiometric information can be recovered from RGB-D images and demonstrate a new role RGB-D sensors can play for general scene understanding tasks.