Academic Journal
A Light Field Depth Estimation Algorithm Considering Blur Features and Prior Knowledge of Planar Geometric Structures
| Title: | A Light Field Depth Estimation Algorithm Considering Blur Features and Prior Knowledge of Planar Geometric Structures |
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| Authors: | Shilong Zhang, Zhendong Liu, Xiaoli Liu, Dongyang Wang, Jie Yin, Jianlong Zhang, Chuan Du, Baocheng Yang |
| Source: | Applied Sciences, Vol 15, Iss 3, p 1447 (2025) |
| Publisher Information: | MDPI AG, 2025. |
| Publication Year: | 2025 |
| Collection: | LCC:Technology LCC:Engineering (General). Civil engineering (General) LCC:Biology (General) LCC:Physics LCC:Chemistry |
| Subject Terms: | light field, multi-focus, feature point matching, depth propagation, photometric consistency, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999 |
| More Details: | Light field camera depth estimation is a core technology for high-precision three-dimensional reconstruction and realistic scene reproduction. We propose a depth estimation algorithm that fuses blurry features and planar geometric structure priors, aimed at overcoming the limitations of traditional methods in neighborhood selection and mismatching in weak texture regions. First, by constructing a multi-constraint adaptive neighborhood microimage set, the microimages with the lowest blur degree are selected to calculate matching costs, and sparse feature correspondence relationships are used to propagate depth information. Second, planar prior knowledge is introduced to optimize pixel matching costs in weak texture regions, and weights are dynamically adjusted and pixel matching costs are updated during the iterative propagation process within microimages based on matching window completeness. Then, potential mismatched points are eliminated using epipolar geometric relationships. Finally, experiments were conducted using public and real-world datasets for verification and analysis. Compared with famous depth estimation algorithms, such as Zeller and BLADE, the Our method demonstrates superior performance in quantitative depth estimation metrics, scene reconstruction completeness, object edge clarity, and depth scene coverage, providing richer and more accurate depth information. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2076-3417 |
| Relation: | https://www.mdpi.com/2076-3417/15/3/1447; https://doaj.org/toc/2076-3417 |
| DOI: | 10.3390/app15031447 |
| Access URL: | https://doaj.org/article/5600e8de91a942e2808bdb26babe8e8d |
| Accession Number: | edsdoj.5600e8de91a942e2808bdb26babe8e8d |
| Database: | Directory of Open Access Journals |
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| ISSN: | 20763417 |
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| DOI: | 10.3390/app15031447 |
| Published in: | Applied Sciences |
| Language: | English |