# One-view occlusion detection for stereo matching with a fully connected CRF model

## TL;DR

This paper introduces a novel one-view occlusion detection method within a fully connected CRF model for stereo matching, improving accuracy and efficiency by reducing the need for dual-view processing.

## Contribution

It proposes the OVOD approach for occlusion detection that simplifies stereo matching by performing a single energy minimization, enhancing accuracy over standard methods.

## Key findings

- Significantly improves stereo matching accuracy on Middlebury dataset.
- Reduces computational cost by avoiding second view energy calculations.
- Achieves state-of-the-art results in median, average, and mean squared error metrics.

## Abstract

In this paper, we extend the standard belief propagation (BP) sequential technique proposed in the tree-reweighted sequential method to the fully connected CRF models with the geodesic distance affinity. The proposed method has been applied to the stereo matching problem. Also a new approach to the BP marginal solution is proposed that we call one-view occlusion detection (OVOD). In contrast to the standard winner takes all (WTA) estimation, the proposed OVOD solution allows to find occluded regions in the disparity map and simultaneously improve the matching result. As a result we can perform only one energy minimization process and avoid the cost calculation for the second view and the left-right check procedure. We show that the OVOD approach considerably improves results for cost augmentation and energy minimization techniques in comparison with the standard one-view affinity space implementation. We apply our method to the Middlebury data set and reach state-of-the-art especially for median, average and mean squared error metrics.