Efficient Stereo Matching Using Histogram Aggregation with Multiple Slant Hypotheses

This paper presents an enhancement to the recent framework of histogram aggregation [1], that enables to improve the matching accuracy while preserving a low computational complexity. The original algorithm uses a fronto-parallel support window for cost aggregation, which leads to inaccurate results in the presence of significant surface slant. We address the problem by considering a pre-defined set of discrete orientation hypotheses for the aggregation window. It is shown that a single orientation hypothesis in the Disparity Space Image is usually representative of a large interval of possible 3D slants, and that handling slant in the disparity space has the advantage of avoiding visibility issues. We also propose a fast recognition scheme in the Disparity Space Image volume for selecting the most likely orientation hypothesis for aggregation. The experiments clearly prove the effectiveness of the approach.