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.

Marelli, D., Bianco, S., Ciocca, G. (2020). IVL-SYNTHSFM-v2: A synthetic dataset with exact ground truth for the evaluation of 3D reconstruction pipelines. DATA IN BRIEF, 29(April 2020) [10.1016/j.dib.2019.105041].

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

Marelli, Davide
;
Bianco, Simone
;
Ciocca, Gianluigi
2020

Abstract

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.
Articolo in rivista - Articolo scientifico
Structure from Motion (SfM),3D reconstruction,Blender,Realistically rendered images
English
23-dic-2019
2020
29
April 2020
105041
none
Marelli, D., Bianco, S., Ciocca, G. (2020). IVL-SYNTHSFM-v2: A synthetic dataset with exact ground truth for the evaluation of 3D reconstruction pipelines. DATA IN BRIEF, 29(April 2020) [10.1016/j.dib.2019.105041].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/255412
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