Fast and Efficient Depth Map Estimation from Light Fields
This work addresses efficient depth estimation for applications like computer vision, but it is incremental as it builds on existing line fitting principles.
The paper tackles depth map estimation from light field images by improving line fitting in 4D space with SGM and Census transform initialization, achieving a significant reduction in computations while preserving low computational time on a single CPU thread.
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.