Improved quadruple sparse census transform and adaptive multi-shape aggregation algorithms for precise stereo matching

Stereo matching is an important computer vision tool for three-dimensional (3D) positioning, 3D visual representation, precision detection and recognition with 3D information. For 3D visual representation, for example, the depth image-based rendering (DIBR) needs high accuracy depth maps to synthesise multiple views for 3D naked-eyes displays. It is hard to predict high-precision disparity for smooth regions and occlusion areas by traditional census-based stereo matching methods. In this work, two new improved algorithms, called the improved quadruple sparse census transform and the adaptive multi-shape aggregation, are proposed to achieve a precise stereo matching system. Instead of a binary threshold, the improved quadruple auxiliary census function with an adaptive threshold and the patch mean can raise the discrimination performance for better stereo matching. The selected sparse census patch is proposed to reduce the computation of the pixel-matching cost. For effective aggregation of matching costs, multi-shape windows and a texture-aware algorithm is suggested to decide a suitable window. The proposed adaptive multi-shape aggregation method performs better and solves the discontinuous depth problem occurring near the object boundaries. Experimental results show that the proposed stereo matching system with the proposed quadruple sparse census transform and multi-shape cost aggregation achieves better raw depth maps than the existing algorithms for stereo matching.

Automated summary

The paper introduces two new stereo matching algorithms that improve the census function and matching aggregation strategy, leading to higher matching accuracy. Experimental results demonstrate that the proposed system outperforms existing stereo matching algorithms in terms of accuracy.