In this paper, we present a technique for recovering a model of shape, illumination, reflectance, and shading from a single image taken from an RGB-D sensor. To do this, we extend the SIRFS (“shape, illumination and reflectance from shading”) model, which recovers intrinsic scene properties from a single image  [1]  . Though SIRFS works well on neatly segmented images of objects, it performs poorly on images of natural scenes which often contain occlusion and spatially-varying illumination. We therefore present Scene-SIRFS, a generalization of SIRFS in which we model a scene using a mixture of shapes and a mixture of illuminations, where those mixture components are embedded in a “soft” segmentation-like representation of the input image. We use the noisy depth maps provided by RGB-D sensors (such as the Microsoft Kinect) to guide and improve shape estimation. Our model takes as input a single RGB-D image and produces as output an improved depth map, a set of surface normals, a reflectance image, a shading image, and a spatially varying model of illumination. The output of our model can be used for graphics applications such as relighting and retargeting, or for more broad applications (recognition, segmentation) involving RGB-D images.
['Jonathan T. Barron', 'Jitendra Malik']
Intrinsic Scene Properties from a Single RGB-D Image