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IVL-SYNTHSFM-v2: A synthetic dataset with exact ground truth for the evaluation of 3D reconstruction pipelines

This article presents a dataset with 4000 synthetic images portraying five 3D models from different viewpoints under varying lighting conditions. Depth of field and motion blur have also been used to generate realistic images. For each object, 8 scenes with different combinations of lighting, depth...

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Detalles Bibliográficos
Autores principales: Marelli, Davide, Bianco, Simone, Ciocca, Gianluigi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6971370/
https://www.ncbi.nlm.nih.gov/pubmed/31993461
http://dx.doi.org/10.1016/j.dib.2019.105041
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author Marelli, Davide
Bianco, Simone
Ciocca, Gianluigi
author_facet Marelli, Davide
Bianco, Simone
Ciocca, Gianluigi
author_sort Marelli, Davide
collection PubMed
description This article presents a dataset with 4000 synthetic images portraying five 3D models from different viewpoints under varying lighting conditions. Depth of field and motion blur have also been used to generate realistic images. For each object, 8 scenes with different combinations of lighting, depth of field and motion blur are created and images are taken from 100 points of view. Data also includes information about camera intrinsic and extrinsic calibration parameters for each image as well as the ground truth geometry of the 3D models. The images were rendered using Blender. The aim of this dataset is to allow evaluation and comparison of different solutions for 3D reconstruction of objects starting from a set of images taken under different realistic acquisition setups.
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spelling pubmed-69713702020-01-28 IVL-SYNTHSFM-v2: A synthetic dataset with exact ground truth for the evaluation of 3D reconstruction pipelines Marelli, Davide Bianco, Simone Ciocca, Gianluigi Data Brief Computer Science This article presents a dataset with 4000 synthetic images portraying five 3D models from different viewpoints under varying lighting conditions. Depth of field and motion blur have also been used to generate realistic images. For each object, 8 scenes with different combinations of lighting, depth of field and motion blur are created and images are taken from 100 points of view. Data also includes information about camera intrinsic and extrinsic calibration parameters for each image as well as the ground truth geometry of the 3D models. The images were rendered using Blender. The aim of this dataset is to allow evaluation and comparison of different solutions for 3D reconstruction of objects starting from a set of images taken under different realistic acquisition setups. Elsevier 2019-12-23 /pmc/articles/PMC6971370/ /pubmed/31993461 http://dx.doi.org/10.1016/j.dib.2019.105041 Text en © 2020 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Computer Science
Marelli, Davide
Bianco, Simone
Ciocca, Gianluigi
IVL-SYNTHSFM-v2: A synthetic dataset with exact ground truth for the evaluation of 3D reconstruction pipelines
title IVL-SYNTHSFM-v2: A synthetic dataset with exact ground truth for the evaluation of 3D reconstruction pipelines
title_full IVL-SYNTHSFM-v2: A synthetic dataset with exact ground truth for the evaluation of 3D reconstruction pipelines
title_fullStr IVL-SYNTHSFM-v2: A synthetic dataset with exact ground truth for the evaluation of 3D reconstruction pipelines
title_full_unstemmed IVL-SYNTHSFM-v2: A synthetic dataset with exact ground truth for the evaluation of 3D reconstruction pipelines
title_short IVL-SYNTHSFM-v2: A synthetic dataset with exact ground truth for the evaluation of 3D reconstruction pipelines
title_sort ivl-synthsfm-v2: a synthetic dataset with exact ground truth for the evaluation of 3d reconstruction pipelines
topic Computer Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6971370/
https://www.ncbi.nlm.nih.gov/pubmed/31993461
http://dx.doi.org/10.1016/j.dib.2019.105041
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