Photometric stereo via locality sensitive high-dimension hashing

In this paper, we extend the new photometric stereo method of Hertzmenn and Seitz that uses many images of an object together with a calibration object. For each point in the registered collection of images, we have a large number of brightness values. Photometric stereo finds a similar collection of brightness values from the calibration object and overdetermines the surface normal. With a large number of images, finding similar brightnesses becomes costly search in high dimensions. To speed up the search, we apply locality sensitive high dimensional hashing (LSH) to compute the irregular target object's surface orientation. The experimental results of a simplified photometric stereo experiment show consistent results in surface orientation. LSH can be implemented very efficiently and offers the possibility of practical photometric stereo computation with a large number of images.