A New Hybrid Stereo Disparity Estimation Algorithm with Guided Image Filtering-Based Cost Aggregation

Abstract

Stereo matching algorithms are useful for estimating a dense depth characteristic of a scene by finding corresponding points from stereo images of the scene. Several factors such as occlusion, noise, and illumination inconsistencies in the scene affect the disparity estimates and make this process challenging. Algorithms developed to overcome these challenges can be broadly categorized as learning-based and non-learning based disparity estimation algorithms. The learning-based approaches are more accurate but computationally expensive. In contrary, non-learning based algorithms are widely used and are computationally efficient algorithms. In this paper, we propose a new stereo matching algorithm using guided image filtering (GIF)-based cost aggregation. The main contribution of our approach is a cost calculation framework which is a hybrid of cross-correlation between stereo-image pairs and scene segmentation (HCS). The performance of our HCS technique was compared with state-ofthe- art techniques using version 3 of the benchmark Middlebury dataset. Our results confirm the effective performance of the HCS technique.