Find research datasets worth reusing

opencc-by-4.0Feb 2020View details →

VR-Together Pilot 1: 3D Animated Character Meshes

This dataset contains the 3D Animated Characters as used for the Pilot 1 experience of the VR-Together project. The animated characters come as a set of FBX files with associated diffuse and normal maps.

3D mesh model and raw images of a drifting iceberg in Dickson Fjord (NE Greenland) on 21 August 2018 at 17:09 UTC

This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Dickson Fjord in northeast Greenland on 21 August 2018. The UAV survey commenced at 17:09 UTC. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_417-419 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats.  A complete file list is provided in the README file that accompanies this dataset. This dataset is discussed in: Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to Remote Sensing.

3D mesh model and raw images of a drifting iceberg in Dickson Fjord (NE Greenland) on 20 August 2018 at 12:41 UTC

This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Dickson Fjord in northeast Greenland on 20 August 2018. The UAV survey commenced at 12:41 UTC. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_493-497 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats.  A complete file list is provided in the README file that accompanies this dataset. This dataset is discussed in: Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to Remote Sensing.

opencc-by-sa-4.0May 2023View details →

3D meshes of wood samples (KUR project)

The repository contains 3D meshes of 83 wood samples from the LEIZA reference collection documented as part of the project &bdquo;Mass Finds in Archaeological Collections“ funded by the Federal Cultural Foundation, from 15.04.2008 to 31.12.2011 within the framework of the "Programme for the Conservation and Restoration of Mobile Cultural Property" (KUR, see www.rgzm.de/kur). Each wood sample has two 3D meshes, one before conservation (KUR_3D_Vorzustand.zip) and one after conservation (KUR_3D_Endzustand.zip). A 3D fringe light projector (GOM ATOS III Rev.01) with a resolution of 0.25 mm was used to digitize the samples. Additional information can be found in the stored metafiles (*.json, *.ttl). The acquisition of 3D data was done during July 2009 - August 2014.

opencc-by-4.0Aug 2023View details →

Meshing strategies for 3D geo-electromagnetic modeling in the presence of metallic infrastructure

Accompanying data to journal article <blockquote> Castillo-Reyes, O., Rulff, P., Schankee Um, E., Amor-Martin, A. (2023) Meshing strategies for 3D geo-electromagnetic modeling in the presence of metallic infrastructure. Accepted for publication in Computational Geosciences. </blockquote>

3D mesh model and raw images of a drifting iceberg in Nuup Kangerlua (Godthåbsfjord), SW Greenland on 9 August 2017

This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Nuup Kangerlua (Godthåbsfjord) in southwest Greenland on 9 August 2017. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_566-DJI_570 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats.  A complete file list is provided in the README file that accompanies this dataset. This dataset is discussed in: Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to Remote Sensing.

3D mesh model and raw images of a drifting iceberg in the Vaigat Strait (NW Greenland) on 3 August 2019

This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in the Vaigat Strat in northwest Greenland on 3 August 2019. The UAV survey commenced at 15:06 UTC. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats.  A complete file list is provided in the README file that accompanies this dataset. This dataset is discussed in: Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to Remote Sensing.

3D mesh model and raw images of a drifting iceberg in the Vaigat Strait (NW Greenland) on 6 August 2019

This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in the Vaigat Strat in northwest Greenland on 6 August 2019. The UAV survey commenced at 11:10 UTC. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats.  A complete file list is provided in the README file that accompanies this dataset. This dataset is discussed in: Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to Remote Sensing.

3D mesh model and raw images of a drifting iceberg in Nuup Kangerlua (Godthåbsfjord), SW Greenland on 22 August 2017

This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Nuup Kangerlua (Godthåbsfjord) in southwest Greenland on 17 August 2017. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_659-662 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats.  A complete file list is provided in the README file that accompanies this dataset. This dataset is discussed in: Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to Remote Sensing.

3D mesh model and raw images of a drifting iceberg in Nuup Kangerlua (Godthåbsfjord), SW Greenland on 17 August 2017

This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Nuup Kangerlua (Godthåbsfjord) in southwest Greenland on 17 August 2017. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_528, 530, 531-533 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats.  A complete file list is provided in the README file that accompanies this dataset. This dataset is discussed in: Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to Remote Sensing.

3D mesh model and raw images of a drifting iceberg in Nuup Kangerlua (Godthåbsfjord), SW Greenland on 11 August 2017

This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Nuup Kangerlua (Godthåbsfjord) in southwest Greenland on 11 August 2017. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_330, 332, 334, 335, 336, 337, 338, 339 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats.  A complete file list is provided in the README file that accompanies this dataset. This dataset is discussed in: Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to Remote Sensing.

opencc-by-4.0Jul 2021View details →

A discrete 3D mesh of the thorax of a newborn with region segmentation

# Newborn mesh ## Description: This dataset contains a labelled mesh of the thorax of a newborm premature human baby with the sectionning of different region of the body. The mesh is composed of 229363 nodes that are grouped into 1356069 tetrahedron elements. ## Original data The original DICOM (Digital Imaging and communications in medicine) images belong to the Cork University Hospital (CUH) data base. The hospital contacted the parents who approved the academic use of the anonymised CT scan of their child, under the ethical approval ECM 4 (gg) 07/03/18 from the Clinical Research Ethics Committee of the Cork Teaching Hospitals. The thoracic CT scan consist of a stack of 367 cross sectional slices, 0.625 mm thick. The area of each image is 512 x 512 pixels with a pixel size of 0.3555 mm. The neonate was born at 36 weeks gestational age with 3.58 kg weight. ## Sectioning process The segmentation of nine main organs (skin, fat, muscle, bone, cartilage, heart, artery, trachea and lung) was done using NIRFASTSlicer2.0. Each DICOM image exhibits the different organs in an especific Housenfield Unit (HU, quantitave scale of radiodensity); dense organs like bone have whitish color, in contrast with less dense organs which appereance is dark. A distinctive colour was assigned to each organ by direct drawing over the CT images one by one. The HU were used to define the boundaries between organs. The organs were considered homogeneous, therefore fine structures present in the human body were ignored. For our study, we subdivided the lungs into 3 different segments (inner, middle and external). On completion of segmentation, the thoracic CT was subdivided in the 11 regions. ## Regions The mesh has been divided in 11 different regions number from 1 to 11 in the following way: <ul> <li>1 -> lung (external segment)</li> <li>2 -> bone</li> <li>3 -> cartilage</li> <li>4 -> heart</li> <li>5 -> muscle</li> <li>6 -> artery</li> <li>7 -> fat</li> <li>8 -> skin</li> <li>9 -> trachea</li> <li>10 -> lung (middle segment)</li> <li>11 -> lung (inner segment)</li> </ul> ## Data format The mesh data is written in 3 different file format: <ul> <li> mesh.mat: MATLAB .mat format that contains 5 variables: <ul> <li>dimension: Number of dimension in the mesh</li> <li>nodes: Matrix of size 229363 x 3 containing the (x,y,z) coordinates of each node in the mesh in milimetres (mm)</li> <li>elements: Matrix of size 1356096 x 4 containing the indices of the nodes corresponding to each element (tetrahedron) of the mesh</li> <li>region: Vector size 229363 x 1 indicating to which region the nodes belongs.</li> <li>bndvtx: Vector of size 229363 x 1 indicating whether the node is on the boundary of the mesh.</li> </ul> </li> <li>mesh.vtk: Mesh in the vtk Datafile Version 2.0 format. The only data field in the VTK file corresponds to the region variable in the .mat file.</li> <li>mesh_csv.zip: Mesh in a .csv format. The zip file contains 4 csv files containing the nodes list, the elements list, the region list and the bndvtx list.</li> </ul> ## Licence The mesh is published under the creative common CC-BY licence. You are free to use this data as you wish, but please cite this dataset using its DOI for example. ## Digital Object Identifier: DOI: 10.5281/zenodo.4916863 ## Authors Mesh sectionning: Andrea Pacheco Data formating: Baptiste Jayet ## Funding The research leading to these results was funded by Science Foundation Ireland project no. SFI/15/RP/2828

opencc-by-4.0May 2023View details →

3D ToF MPI multi-frequency mesh dataset

The accuracy of indirect 3D Time-of-Flight (3D ToF) measurements is often limited by multi-path interferences (MPI) caused by multi-layer ToF conditions. Taking multiple measurements of the same scene at different modulation frequencies allows separating the interfering signal components of the individual paths according to several optimization methods described in literature. Orthogonal matching pursuit (OMP) optimization has been reported to achieve good path separation performance and superior results compared to particle swarm optimization (PSO). This work presents improved PSO performance for MPI separation based on new experimental data and refined PSO strategy. The current PSO approach achieves good distance separation in the setup used with low RMS distance errors in the order of 20 cm in situations where the OMP approach shows RMS errors higher than 100 cm. The previously reported minimum distance difference limitation between two separate objects of 2.7 m for the OMP algorithm could be reduced to roughly 0.75 m for the PSO algorithm. The trade-off between image accuracy and computing effort is explored and presented with respect to PSO parameter settings. This dataset provides researchers with measurement data to develop their own multi-layer algorithms and contribute to the ongoing development of great 3D ToF cameras.

opencc-by-4.0Jul 2014View details →

Meshes and initial data for 2D and 3D experiments with PyNosh

Data files used in Experiments with PyNosh.

opencc-by-4.0Mar 2017View details →

In-memory integration of existing software components for parallel adaptive unstructured mesh workflows: PHASTA 3D dam break

Input mesh, solution field, configuration files, and job scripts for running the adaptive PHASTA-Chef 3D dam break case. Data stream, POSIX file, and ramdisk results are also included. The python scripts used to generate the bandwidth and open/close time plots are included.

opencc-byAug 2020View details →

Arkadiou 198 Doorway 3D Mesh Lab

The doorway at Arkadiou 198 Street consists of a round marble semicircular arch of a transitional Venetian-Ottoman style, stone pillars and decorated elements. It was constructed of heavy masonry and the arch is built of two symmetrical pieces and a smaller part in the middle completes the structure. The pillars are simple and straight and bear a capital with moderate foliate decoration. The lintel is undecorated, a wide molding, and the anthem frame has been transformed into the alem coat of arms the tabular door with the point of junction decorated with two leaves, characteristic of Ottoman architecture. Source: Objaverse 1.0 / Sketchfab

opencc-byOct 2016View details →

Heritage library in Gliwice - 3D textured mesh

3d textured mesh of heritage library destined for demolition. Model created by POI Format from photos taken with UAV, processed with photogrammetric methods. Source: Objaverse 1.0 / Sketchfab

opencc-byMar 2017View details →

Garrovera Nave Simplified 3d Mesh

Estas naves forman parte de un complejo abandonado donde se sacaba provecho a la algarroba. Se encuentra en es Pont d'Inca, Marratxí, Mallorca. Y tuvimos la oportunidad de inmortalizar su estado antes de que las obras de reforma lo transformen en un centro de diversos usos sociales y culturales. Source: Objaverse 1.0 / Sketchfab

opencc-by-nc-4.0Nov 2017View details →

Data from: Diphthong Synthesis using the Dynamic 3D Digital Waveguide Mesh

This dataset comprises the data and MATLAB scripts associated with the paper "Diphthong Synthesis using the Dynamic 3D Digital Waveguide Mesh" published in IEEE/ACM Transactions on Audio, Speech and Language Processing. The dataset contains: <ul> <li>Audio files in .wav format including simulation outputs and recorded microphone and electrolaryngograph signals</li> <li>MATLAB files for simulation (requires GPU device) and plotting results, including volume matrices for 6 English monophthongs, obtained from MRI images as described in the paper</li> <li>Plots of results in .png and MATLAB .fig format</li> </ul> Contact: amelia.gully@york.ac.uk