Fully Parallel Architecture for Semi-global Stereo Matching with Refined Rank Method
This work addresses hardware efficiency for stereo matching in computer vision applications, but it appears incremental as it builds on existing SGM methods with refinements.
The paper tackles efficient stereo matching by proposing a fully parallel architecture for semi-global matching (SGM) with a refined rank method, resulting in excellent subjective quality and objective performance suitable for VLSI implementation.
Fully parallel architecture at disparity-level for efficient semi-global matching (SGM) with refined rank method is presented. The improved SGM algorithm is implemented with the non-parametric unified rank model which is the combination of Rank filter/AD and Rank SAD. Rank SAD is a novel definition by introducing the constraints of local image structure into the rank method. As a result, the unified rank model with Rank SAD can make up for the defects of Rank filter/AD. Experimental results show both excellent subjective quality and objective performance of the refined SGM algorithm. The fully parallel construction for hardware implementation of SGM is architected with reasonable strategies at disparity-level. The parallelism of the data-stream allows proper throughput for specific applications with acceptable maximum frequency. The results of RTL emulation and synthesis ensure that the proposed parallel architecture is suitable for VLSI implementation.