PASMVS : a perfectly accurate, synthetic, path-traced dataset featuring specular material properties for multi-view stereopsis training and reconstruction applications

We are excited to announce that the repository will soon undergo an upgrade, featuring a new look and feel along with several enhanced features to improve your experience. Please be on the lookout for further updates and announcements regarding the launch date. We appreciate your support and look forward to unveiling the improved platform soon.

Show simple item record

dc.contributor.author Broekman, Andre
dc.contributor.author Grabe, Petrus Johannes
dc.date.accessioned 2020-10-23T10:45:41Z
dc.date.available 2020-10-23T10:45:41Z
dc.date.issued 2020-10
dc.description Research data for this article. PASMVS: a dataset for multi-view stereopsis training and reconstruction applications Original Data: A large collection of synthetic, path-traced renderings for use in multi-view stereopsis and 3D reconstruction applications. The material properties are primarily non-Lambertian (reflective metals). Ground truth depth maps, model geometry, object masks and camera extrinsic and intrinsic data is provided together with the rendered images. A total of 18000 samples are provided (45 camera views for 400 scenes), varying the illumination, geometry models, materials properties and camera focal length. Repository name: Mendeley Data. Data identification number: 10.17632/fhzfnwsnzf.2 URL: https://data.mendeley.com/datasets/fhzfnwsnzf/2 en_ZA
dc.description.abstract A Perfectly Accurate, Synthetic dataset for Multi-View Stere- opsis (PASMVS) is presented, consisting of 400 scenes and 18,0 0 0 model renderings together with ground truth depth maps, camera intrinsic and extrinsic parameters, and binary segmentation masks. Every scene is rendered from 45 differ- ent camera views in a circular pattern, using Blender’s path- tracing rendering engine. Every scene is composed from a unique combination of two camera focal lengths, four 3D models of varying geometrical complexity, five high defini- tion, high dynamic range (HDR) environmental textures to replicate photorealistic lighting conditions and ten materials. The material properties are primarily specular, with a selec- tion of more diffuse materials for reference. The combination of highly specular and diffuse material properties increases the reconstruction ambiguity and complexity for MVS recon- struction algorithms and pipelines, and more recently, state- of-the-art architectures based on neural network implemen- tations. PASMVS serves as an addition to the wide spectrum of available image datasets employed in computer vision re- search, improving the precision required for novel research applications. en_ZA
dc.description.department Civil Engineering en_ZA
dc.description.librarian pm2020 en_ZA
dc.description.sponsorship The Chair in Railway Engineering in the Department of Civil Engineering at the University of Pretoria. en_ZA
dc.description.uri http://www.elsevier.com/locate/dib en_ZA
dc.identifier.citation Broekman, A. & Gräbe, P.J. 2020, 'PASMVS : a perfectly accurate, synthetic, path-traced dataset featuring specular material properties for multi-view stereopsis training and reconstruction applications', Data in Brief, vol. 32. art. 106219, pp. 1-9. en_ZA
dc.identifier.issn 2352-3409 (online)
dc.identifier.other 10.1016/j.dib.2020.106219
dc.identifier.uri http://hdl.handle.net/2263/76586
dc.language.iso en en_ZA
dc.publisher Elsevier en_ZA
dc.rights © 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CCBY license. en_ZA
dc.subject Multi-view stereopsis en_ZA
dc.subject 3D reconstruction en_ZA
dc.subject Synthetic data en_ZA
dc.subject Ground truth depth map en_ZA
dc.subject Blender en_ZA
dc.subject Perfectly accurate, synthetic dataset for multi-view stereopsis (PASMVS) en_ZA
dc.title PASMVS : a perfectly accurate, synthetic, path-traced dataset featuring specular material properties for multi-view stereopsis training and reconstruction applications en_ZA
dc.type Article en_ZA


Files in this item

This item appears in the following Collection(s)

Show simple item record