Distributed dense stereo matching for 3D reconstruction using parallel-based processing advantages

Instead of measuring photo-similarity, SymStereo is a stereo vision algorithm that uses new cost functions to measure symmetry differences between pairs of images. In this paper we propose the acceleration of a complete signal processing pipeline for generating 3D volumes based on dense SymStereo. The outputs here generated achieve superior reconstruction quality namely for slant based scenarios, so typical in autonomous systems, that have to capture pairs of images and perform moving decisions in real-time. In particular, we analyse several parallelization strategies for the compute-intensive aggregation procedure using different parameters and evaluate a trade-off between processing time, and higher precision of the calculated depths and quality of the final reconstructed 3D volume. The developed parallel pipeline allows to process more than 4.5 volumes per second for high resolution images using commodity GPUs, which conveniently suits its application in a variety of robotics systems.