Local Stereo Matching with 3D Adaptive Cost Aggregation for Slanted Surface Modeling and Sub-pixel Accuracy

This paper presents a new local binocular stereo algorithm which takes into consideration plane fitting at the per-pixel level. Two disparity calculation passes are used. The first pass assumes that surfaces in the scene are fronto-parallel and generates an initial disparity map, from which the disparity plane orientations of all pixels are extracted and refined. In the second pass, the cost aggregation for each pixel is conducted along the estimated disparity plane orientations, rather than the fronto-parallel ones. Large window size with adaptive support weights is used to ensure the effectiveness of the slanted surface modeling. The disparity search space is also quantized at sub-pixel level to improve the accuracy of the disparity results. The experimental results demonstrate the validity of our presented approach.