Proceedings ArticleDOI
Dataset and Pipeline for Multi-view Light-Field Video
Neus Sabater,Guillaume Boisson,Benoit Vandame,Paul Kerbiriou,Frederic Babon,Matthieu Hog,Remy Gendrot,Tristan Langlois,Olivier Bureller,Arno Schubert,Valerie Allie +10 more
- pp 1743-1753
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TLDR
A dataset and a complete pipeline for Light-Field video algorithms specially tailored to process sparse and wide-baseline multi-view videos captured with a camera rig and a depth-based rendering algorithm for Dynamic Perspective Rendering are proposed.Abstract:
The quantity and diversity of data in Light-Field videos makes this content valuable for many applications such as mixed and augmented reality or post-production in the movie industry. Some of such applications require a large parallax between the different views of the Light-Field, making the multi-view capture a better option than plenoptic cameras. In this paper we propose a dataset and a complete pipeline for Light-Field video. The proposed algorithms are specially tailored to process sparse and wide-baseline multi-view videos captured with a camera rig. Our pipeline includes algorithms such as geometric calibration, color homogenization, view pseudo-rectification and depth estimation. Such elemental algorithms are well known by the state-of-the-art but they must achieve high accuracy to guarantee the success of other algorithms using our data. Along this paper, we publish our Light-Field video dataset that we believe may be of special interest for the community. We provide the original sequences, the calibration parameters and the pseudo-rectified views. Finally, we propose a depth-based rendering algorithm for Dynamic Perspective Rendering.read more
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Journal ArticleDOI
X-Fields: implicit neural view-, light- and time-image interpolation
TL;DR: The key idea to make this workable is a NN that already knows the "basic tricks" of graphics in a hard-coded and differentiable form, leading to a compact set of trainable parameters and hence real-time navigation in view, time and illumination.
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X-Fields: Implicit Neural View-, Light- and Time-Image Interpolation
TL;DR: The key idea to make this workable is a NN that already knows the "basic tricks" of graphics in a hard-coded and differentiable form, leading to a compact set of trainable parameters and hence real-time navigation in view, time and illumination.
Journal ArticleDOI
Dense Light Field Coding: A Survey
TL;DR: A comprehensive survey of the most relevant LF coding solutions proposed in the literature, focusing on angularly dense LFs, and comprehensive insights are presented into open research challenges and future research directions for LF coding.
Journal ArticleDOI
A Benchmark of DIBR Synthesized View Quality Assessment Metrics on a New Database for Immersive Media Applications
TL;DR: A new DIBR-synthesized image database with the associated subjective scores is presented and subjective test results show that the interview synthesis methods, having more input information, significantly outperform the single-view-based ones.
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.
References
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