Journal ArticleDOI
Depth Estimation From Light Field Using Graph-Based Structure-Aware Analysis
TLDR
A novel two-stage light field depth estimation method via graph spectral analysis to exploit the complete correlations and dependencies within angular patches and spatial images to improve the depth map estimation, especially in the edge regions.Abstract:
Existing light field depth map estimation approaches only utilize partial angular views in occlusion areas and local spatial dependencies in the optimization. This paper proposes a novel two-stage light field depth estimation method via graph spectral analysis to exploit the complete correlations and dependencies within angular patches and spatial images. The initial depth map estimation leverages the undirected graph to jointly consider occluded and unoccluded views within each angular patch. The estimated depth minimizes the structural incoherence of its corresponding angular patch with the focused one by evaluating the highest graph frequency component. Subsequently, depth map refinement optimizes the initial depth map with the color consistency and smoothness formulated by weighted adjacency matrix. The structural constraints are efficiently employed using low-pass graph filtering with Chebyshev polynomial approximation. Experimental results demonstrate that the proposed method improves the depth map estimation, especially in the edge regions.read more
Citations
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Journal ArticleDOI
UrbanLF: A Comprehensive Light Field Dataset for Semantic Segmentation of Urban Scenes
TL;DR: A high-quality and challenging urban scene dataset, containing 1074 samples composed of real-world and synthetic light field images as well as pixel-wise annotations for 14 semantic classes, is proposed, believed to be the largest and the most diverse light field dataset for semantic segmentation.
Journal ArticleDOI
Overview and Efficiency of Decoder-Side Depth Estimation in MPEG Immersive Video
Dawid Mieloch,Patrick Garus,Marta Milovanovic,Jun Young Jeong,Smitha Lingadahalli Ravi,Basel Salahieh +5 more
TL;DR: The combined non-transmission of depth maps and efficient coding of textures enabled by the DSDE leads to efficient compression and rendering quality improvement compared to the usual encoder-side depth estimation.
Journal ArticleDOI
Geometry Auxiliary Salient Object Detection for Light Fields via Graph Neural Networks
TL;DR: Zhang et al. as mentioned in this paper proposed a light field salient object detection approach that formulates the geometric coherence among multiple views of light fields as graphs, where the angular/central views represent the nodes and their relations compose the edges.
Journal ArticleDOI
Occlusion-Aware Unsupervised Learning of Depth From 4-D Light Fields
TL;DR: LFDE-OccUnNet as mentioned in this paper proposes an unsupervised learning-based method, which does not require ground-truth depth as supervision during training, and adopts a multi-scale network with a weighted smoothness loss to handle the textureless areas.
Journal ArticleDOI
Overview and Efficiency of Decoder-Side Depth Estimation in MPEG Immersive Video
TL;DR: In this article , the authors present the overview and rationale behind the Decoder-Side Depth Estimation (DSDE) mode of the MPEG Immersive Video (MIV) standard, using the Geometry Absent profile, for efficient compression of immersive multiview video.
References
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Journal ArticleDOI
Discrete Signal Processing on Graphs
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Journal ArticleDOI
Exact optimization for Markov random fields with convex priors
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Proceedings ArticleDOI
Depth from Combining Defocus and Correspondence Using Light-Field Cameras
TL;DR: A novel simple and principled algorithm is presented that computes dense depth estimation by combining both defocus and correspondence depth cues, and shows how to combine the two cues into a high quality depth map, suitable for computer vision applications such as matting, full control of depth-of-field, and surface reconstruction.