Dense spatio‐temporal stereo matching for intelligent driving systems

Abstract

This paper addresses the problem of matching stereo images acquired by a stereo system mounted aboard an intelligent vehicle. The main idea behind the new method consists in involving temporal matching between a current stereo pair and its preceding one to achieve the spatial matching of the former stereo by involving the matching results obtained at the last frame. The proposed method is achieved in three main steps. First, an edge based disparity map is derived from the disparity map of the preceding frame, we call it the assisting disparity map (ADM). Second, for each scan‐line, a set of local ranges and global ranges are deduced from the ADM to keep only potential matching candidates. Third, the matching is done on the basis of dynamic programming algorithm by involving in the resulting local and global ranges we get from the last step. The proposed approach has been tested on both real and synthetic stereo sequences and the results demonstrate its effectiveness.

Dense stereo matching in depth discontinuity areas based on edge detection and nearest valid point search

“…A novel coarse-to-fine matching strategy has been proposed to perform a pixel-wise correspondence search across stereo image pairs on the basis of the sparse optical flow field estimation and fastguided filter refinement (Yuan et al 2019). A spatio-temporal method for intelligent driving systems considers the temporal consistency between adjacent frames (Kerkaou et al 2021). In recent years, deep neural networks have been extensively investigated to learn disparity maps from two stereo images in an end-to-end manner (Kang et al 2019).…”