Illumination and camera invariant stereo matching

Color information can be used as a basic and crucial cue for finding correspondence in a stereo matching algorithm. In a real scene, however, image colors are affected by various geometric and radiometric factors. For this reason, the raw color recorded by a camera is not a reliable cue, and the color consistency assumption is no longer valid between stereo images in real scenes. Hence the performance of most conventional stereo matching algorithms can be severely degraded under the radiometric variations. In this paper, we present a new stereo matching algorithm that is invariant to various radiometric variations between left and right images. Unlike most stereo algorithms, we explicitly employ the color formation model in our framework and propose a new measure called adaptive normalized cross correlation (ANCC) for a robust and accurate correspondence measure. ANCC is invariant to lighting geometry, illuminant color and camera parameter changes between left and right images, and does not suffer from fattening effects unlike conventional normalized cross correlation (NCC). Experimental results show that our algorithm outperforms other stereo algorithms under severely different radiometric conditions between stereo images.