Fast and Efficient Depth Map Estimation from Light Fields

TL;DR

This paper introduces a fast, CPU-based algorithm for depth map estimation from light field images that enhances line fitting with semi-global matching, significantly reducing computational time while maintaining accuracy.

Contribution

The proposed method improves depth estimation efficiency by combining line fitting with semi-global matching, requiring only a single CPU thread and reducing computational complexity.

Findings

Significant reduction in computation time compared to existing methods

Effective depth map estimation with low computational resources

Preservation of accuracy while improving efficiency

Abstract

The paper presents an algorithm for depth map estimation from the light field images in relatively small amount of time, using only single thread on CPU. The proposed method improves existing principle of line fitting in 4-dimensional light field space. Line fitting is based on color values comparison using kernel density estimation. Our method utilizes result of Semi-Global Matching (SGM) with Census transform-based matching cost as a border initialization for line fitting. It provides a significant reduction of computations needed to find the best depth match. With the suggested evaluation metric we show that proposed method is applicable for efficient depth map estimation while preserving low computational time compared to others.