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GVLD: A Fast and Accurate GPU-Based Variational Light-Field Disparity Estimation Approach.
- Source :
- IEEE Transactions on Circuits & Systems for Video Technology; Jul2021, Vol. 31 Issue 7, p2562-2574, 13p
- Publication Year :
- 2021
-
Abstract
- Disparity estimation is an essential task taking part in many light-field applications. Due to the complexity of algorithms and high dimensional property of light-field data, performing this task involves a significant computational effort and results in very long processing time on CPU. Graphics processing units (GPUs), which is capable of massively parallel processing, is a promising solution to cover the computation requirement and speed up the task. In this paper, we develop a GPU-accelerated approach for light-field disparity estimation using a variational computation framework (GVLD). Our algorithm combines the intrinsic sub-pixel precision of variational formulation and the effectiveness of weighted median filtering to produce a highly accurate solution. The proposed algorithm is fully parallelized and optimized for the implementation using the OpenCL framework. An intensive evaluation including a quantitative comparison to related works and a detailed analysis of the proposed approach’s performance is presented. Experimental results demonstrate our superior performance compared to state-of-the-art approaches. The proposed approach is 10+ times faster than other approaches running on a similar GPU platform and provides the most accurate solution among optimization-based approaches. Compared to the implementation running on CPU, our GPU-accelerated method achieves up to $365\times $ speed up. [ABSTRACT FROM AUTHOR]
- Subjects :
- GRAPHICS processing units
ALGORITHMS
PARALLEL processing
Subjects
Details
- Language :
- English
- ISSN :
- 10518215
- Volume :
- 31
- Issue :
- 7
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Circuits & Systems for Video Technology
- Publication Type :
- Academic Journal
- Accession number :
- 151282999
- Full Text :
- https://doi.org/10.1109/TCSVT.2020.3028258