A quantitative approach for comparison and evaluation of light field rendering techniques

Light field rendering (LFR) is an active research area in computer vision and computer graphics. LFR plays a crucial role in free viewpoint video systems (FW). Although several rendering algorithms have been suggested for LFR but the lack of appropriate datasets with known ground truth has prevented a comparison and evaluation study of LFR algorithms. In most of the LFR papers the method is applied to several test cases for validation and as a result, just a subjective visualized output is given. To overcome this problem, this paper presents a quantitative approach for comparison and evaluation of LFR algorithms. The core of the proposed methodology is a simulation model and a 3D engine. The platform produces the reference images and ground truth data for a given 3D model. Subsequently, data are injected to a comparison engine to compare synthesized images from light field engine with original images from simulation, generating objective results for evaluation. The methodology is highly flexible and efficient to automatically generate LFR datasets and objectively compare and analyze any subset of LFR methods for any given experiment design scheme. Five key rendering algorithms are evaluated with proposed methodology to validate it. Overall, it is shown that the proposed quantitative methodology could be used for LFR objective evaluation and comparison.