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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.02043 |
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| _version_ | 1866912374768795648 |
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| author | Lourenço, Rui Thomaz, Lucas Silva, Eduardo A. B. Faria, Sergio M. M. |
| author_facet | Lourenço, Rui Thomaz, Lucas Silva, Eduardo A. B. Faria, Sergio M. M. |
| contents | Light field cameras and multi-camera arrays have emerged as promising solutions for accurately estimating depth by passively capturing light information. This is possible because the 3D information of a scene is embedded in the 4D light field geometry. Commonly, depth estimation methods extract this information relying on gradient information, heuristic-based optimisation models, or learning-based approaches. This paper focuses mainly on explicitly understanding and exploiting 4D geometrical cues for light field depth estimation. Thus, a novel method is proposed, based on a non-learning-based optimisation approach for depth estimation that explicitly considers surface normal accuracy and occlusion regions by utilising a fully explainable 4D geometric model of the light field. The 4D model performs depth/disparity estimation by determining the orientations and analysing the intersections of key 2D planes in 4D space, which are the images of 3D-space points in the 4D light field. Experimental results show that the proposed method outperforms both learning-based and non-learning-based state-of-the-art methods in terms of surface normal angle accuracy, achieving a Median Angle Error on planar surfaces, on average, 26.3$\%$ lower than the state-of-the-art, and still being competitive with state-of-the-art methods in terms of MSE ${\times}$ 100 and Badpix 0.07. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_02043 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Iterative Occlusion-Aware Light Field Depth Estimation using 4D Geometrical Cues Lourenço, Rui Thomaz, Lucas Silva, Eduardo A. B. Faria, Sergio M. M. Image and Video Processing Computer Vision and Pattern Recognition Light field cameras and multi-camera arrays have emerged as promising solutions for accurately estimating depth by passively capturing light information. This is possible because the 3D information of a scene is embedded in the 4D light field geometry. Commonly, depth estimation methods extract this information relying on gradient information, heuristic-based optimisation models, or learning-based approaches. This paper focuses mainly on explicitly understanding and exploiting 4D geometrical cues for light field depth estimation. Thus, a novel method is proposed, based on a non-learning-based optimisation approach for depth estimation that explicitly considers surface normal accuracy and occlusion regions by utilising a fully explainable 4D geometric model of the light field. The 4D model performs depth/disparity estimation by determining the orientations and analysing the intersections of key 2D planes in 4D space, which are the images of 3D-space points in the 4D light field. Experimental results show that the proposed method outperforms both learning-based and non-learning-based state-of-the-art methods in terms of surface normal angle accuracy, achieving a Median Angle Error on planar surfaces, on average, 26.3$\%$ lower than the state-of-the-art, and still being competitive with state-of-the-art methods in terms of MSE ${\times}$ 100 and Badpix 0.07. |
| title | Iterative Occlusion-Aware Light Field Depth Estimation using 4D Geometrical Cues |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.02043 |