Photometric Refinement of Depth Maps for Multi-Albedo Objects

Avishek Chatterjee, Venu Madhav Govindu; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 933-941

In this paper, we propose a novel uncalibrated photometric method for refining depth maps of multi-albedo objects obtained from consumer depth cameras like Kinect. Existing uncalibrated photometric methods either assume that the object has constant albedo or rely on segmenting images into constant albedo regions. The method of this paper does not require the constant albedo assumption and we believe it is the first work of its kind to handle objects with arbitrary albedo under uncalibrated illumination. We first robustly estimate a rank 3 approximation of the observed brightness matrix using an iterative reweighting method. Subsequently, we factorize this rank reduced brightness matrix into the corresponding lighting, albedo and surface normal components. The proposed factorization is shown to be convergent. We experimentally demonstrate the value of our approach by presenting highly accurate three-dimensional reconstructions of a wide variety of objects. Additionally, since any photometric method requires a radiometric calibration of the camera used, we also present a direct radiometric calibration technique for the infra-red camera of the structured-light stereo depth scanner. Unlike existing methods, this calibration technique does not depend on a known calibration object or on the properties of the scene illumination used.