# Depth Estimation using Modified Cost Function for Occlusion Handling

## TL;DR

This paper introduces a modified cost function for depth estimation that effectively handles occlusions by dynamically updating similarity metrics based on occlusion detection, leading to improved virtual view quality.

## Contribution

It proposes a novel occlusion handling method in depth estimation using a modified similarity cost function that updates during optimization to consider only non-occluded regions.

## Key findings

- Achieved 1.25 dB improvement in virtual view quality.
- Effectively detects occlusions using synthesized side view depth maps.
- Enhances depth estimation accuracy in 3D video sequences.

## Abstract

The paper presents a novel approach to occlusion handling problem in depth estimation using three views. A solution based on modification of similarity cost function is proposed. During the depth estimation via optimization algorithms like Graph Cut similarity metric is constantly updated so that only non-occluded fragments in side views are considered. At each iteration of the algorithm non-occluded fragments are detected based on side view virtual depth maps synthesized from the best currently estimated depth map of the center view. Then similarity metric is updated for correspondence search only in non-occluded regions of the side views. The experimental results, conducted on well-known 3D video test sequences, have proved that the depth maps estimated with the proposed approach provide about 1.25 dB virtual view quality improvement in comparison to the virtual view synthesized based on depth maps generated by the state-of-the-art MPEG Depth Estimation Reference Software.