Occlusion-Aware Depth Estimation Using Light-Field Cameras

By Tingxian Wang, Alexei A. Efros, Ravi Ramamoorthi. IEEE International Conference on Computer Vision, 2015

348 citations recorded by the source corpus.

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A Dataset and Evaluation Methodology for Depth Estimation on 4D Light Fields (2016, methodology): “We show algorithm results of one multi-view algorithm (MV) and four light field algorithms (LF, LF OCC, EPI2, EPI1).”
Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations (2021, background): “Note that this consistency also implies that depth maps could be extracted from our model [34, 47, 45], see also [38].”
Learning-based view synthesis for light field cameras (2016, unknown): “Since our disparity estimation CNN is trained to directly minimize the synthesis error, our estimated disparities are more suitable for this application than existing disparity estimation techniques [Wanner and Goldluecke 2012; Tao et al. 2013; Wang et al. 2015; Jeon et al. 2015] (see Fig.”
Soft 3D reconstruction for view synthesis (2017, methodology): “The most common approach of using the best half of costs to approximate the non-occluded costs [Kang et al. 2001; Wang et al. 2015] introduces new ambiguities even for foreground objects.”
Light Field Image Processing: An Overview (2017, background): “MRF are widely used for problems that involve feature point matching [96]–[99], [102].”
Light Field Super-Resolution with Zero-Shot Learning (2021, methodology): “Specifically, we adopt a representative non-learning-based light field depth estimation algorithm OCC [11] for the evaluation.”