Benchmarking of several disparity estimation algorithms for light field processing

Faezeh Sadat Zakeri; Michel Bätz; Tobias Jaschke; Joachim Keinert; Alexandra Chuchvara

doi:10.1117/12.2521747

16 July 2019 Benchmarking of several disparity estimation algorithms for light field processing

Faezeh Sadat Zakeri, Michel Bätz, Tobias Jaschke, Joachim Keinert, Alexandra Chuchvara

Proceedings Volume 11172, Fourteenth International Conference on Quality Control by Artificial Vision; 111721C (2019) https://doi.org/10.1117/12.2521747
Event: Fourteenth International Conference on Quality Control by Artificial Vision, 2019, Mulhouse, France

Abstract

A number of high-quality depth imaged-based rendering (DIBR) pipelines have been developed to reconstruct a 3D scene from several images taken from known camera viewpoints. Due to the specific limitations of each technique, their output is prone to artifacts. Therefore, the quality cannot be ensured. To improve the quality of the most critical and challenging image areas, an exhaustive comparison is required. In this paper, we consider three questions of benchmarking the quality performance of eight DIBR techniques on light fields: First, how does the density of original input views affect the quality of the rendered novel views? Second, how does disparity range between adjacent input views impact the quality? Third, how does each technique behave for different object properties? We compared and evaluated the results visually as well as quantitatively (PSNR, SSIM, AD, and VDP2). The results show some techniques outperform others in different disparity ranges. The results also indicate using more views not necessarily results in visually higher quality for all critical image areas. Finally, we have shown a comparison for different scene’s complexity such as non-Lambertian objects.

Citation Download Citation

Faezeh Sadat Zakeri, Michel Bätz, Tobias Jaschke, Joachim Keinert, and Alexandra Chuchvara "Benchmarking of several disparity estimation algorithms for light field processing", Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 111721C (16 July 2019); https://doi.org/10.1117/12.2521747

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