Robust Adaptive Normalized Cross-Correlation for Stereo Matching Cost Computation

Stereo matching is a challenging task because stereo images are affected by many factors, such as radiometric distortion, sun and rain flare, flying snow, occlusion, textureless and noisy image regions, and object boundaries. However, most of the existing methods for stereo matching aim to solve only one specific problem. As a result, their performance is degraded significantly when operating with stereo images captured under a variety of scenes and conditions. In this paper, we propose a novel matching cost function based on adaptive normalized cross-correlation (ANCC). We demonstrate several weaknesses of ANCC and propose techniques to resolve them. In addition, we employ available information, such as intensity mean, intensity variance, and support window radius, to estimate the parameters of the proposed matching cost function. Compared with ANCC, the proposed matching cost function reduces the error rates from 24.1% to 17.8% in the Middlebury data set and from 64.1% to 26.4% in the KITTI data set. In addition, for noisy stereo pairs, the proposed function reduces the error rate from 73.6% to 37.3%. The qualitative and quantitative experimental results based on stereo images in different data sets under various conditions show that our proposed matching cost function outperforms state-of-the-art matching cost functions in indoor and outdoor stereo images having various radiometric distortions.