Implementation of Real-Time Post-Processing for High-Quality Stereo Vision

Abstract

We propose a novel post‐processing algorithm and its very‐large‐scale integration architecture that simultaneously uses the passive and active stereo vision information to improve the reliability of the three‐dimensional disparity in a hybrid stereo vision system. The proposed architecture consists of four steps — left‐right consistency checking, semi‐2D hole filling, a tiny adaptive variance checking, and a 2D weighted median filter. The experimental results show that the error rate of the proposed algorithm (5.77%) is less than that of a raw disparity (10.12%) for a real‐world camera image having a 1,280×720 resolution and maximum disparity of 256. Moreover, for the famous Middlebury stereo image sets, the proposed algorithm's error rate (8.30%) is also less than that of the raw disparity (13.7%). The proposed architecture is implemented on a single commercial field‐programmable gate array using only 13.01% of slice resources, which achieves a rate of 60 fps for 1,280×720 stereo images with a disparity range of 256.

Hierarchical bilateral filtering based disparity estimation for view synthesis

“…Among many edge-preserving filters, the bilateral filter [4] is the most popular one and is widely used [7,9]. The bilateral filter is a normalized convolution in which the weighting for each pixel is determined by the spatial distance from the center pixel , as well as its relative difference in intensity.…”