Light Field Segmentation From Super-pixel Graph Representation

TL;DR

This paper introduces a novel super-pixel graph representation for light field segmentation, significantly improving efficiency and accuracy by reducing data redundancy and graph size, and employing graph-cuts for segmentation.

Contribution

The paper presents a new LFSP-based graph structure that enhances light field segmentation efficiency and accuracy over previous methods.

Findings

Outperforms previous algorithms in accuracy

Reduces computational complexity

Effective on both synthetic and real datasets

Abstract

Efficient and accurate segmentation of light field is an important task in computer vision and graphics. The large volume of input data and the redundancy of light field make it an open challenge. In the paper, we propose a novel graph representation for interactive light field segmentation based on light field super-pixel (LFSP). The LFSP not only maintains light field redundancy, but also greatly reduces the graph size. These advantages make LFSP useful to improve segmentation efficiency. Based on LFSP graph structure, we present an efficient light field segmentation algorithm using graph-cuts. Experimental results on both synthetic and real dataset demonstrate that our method is superior to previous light field segmentation algorithms with respect to accuracy and efficiency.