A Framework for Automatically Recovering Object Shape, Reflectance and Light Sources from Calibrated Images

Abstract

In this paper, we present a complete framework for recovering an object shape, estimating its reflectance properties and light sources from a set of images. The whole process is performed automatically. We use the shape from silhouette approach proposed by R. Szeliski (1993) combined with image pixels for reconstructing a triangular mesh according to the marching cubes algorithm. A classification process identifies regions of the object having the same appearance. For each region, a single point or directional light source is detected. Therefore, we use specular lobes, lambertian regions of the surface or specular highlights seen on images. An identification method jointly (i) decides what light sources are actually significant and (ii) estimates diffuse and specular coefficients for a surface represented by the modified Phong model (Lewis, 1994). In order to validate our algorithm efficiency, we present a case study with various objects, light sources and surface properties. As shown in the results, our system proves accurate even for real objects images obtained with an inexpensive acquisition system.

Emptying, refurnishing, and relighting indoor spaces

“…Ramamoorthi and Hanrahan [2001] theoretically analyze the recovery of distant lighting and reflectance using a signal processing approach, and show when this simultaneous recovery is well-conditioned. Beyond distant illumination, several works recover point and directional light source positions simultaneously with reflectance parameters from multiple images of an object of interest [Mercier et al 2007;Xu and Wallace 2008].…”

Online Tracking of Outdoor Lighting Variations for Augmented Reality with Moving Cameras

“…Assuming a correct photometric calibration, a pixel directly measures the intensity resulting from the influence of geometry, reflective property, and incident lighting. In a controlled environment, it is possible to add further assumptions that allow such a recovery of the three components, namely BRDFs, 3D geometry and lighting ( [21,4]). It relies in general on an iterative optimization process to estimate one after the other two components.…”

Illumination Recovery From Image With Cast Shadows Via Sparse Representation

“…Much more accurate multiple illumination information is extracted from the shading of a sphere [47]. In [22], a framework is proposed to automatically recover an object shape, reflectance properties and light sources from a set of images. A unified framework to estimate both distant and point light sources is proposed in [48].…”