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66 results for “3D Digitization”
Lipschitz quaternions in the range [−10, 10]^4, which induce bijective 3D digitized rotations
<p>The file contains Lipschitz quaternions in the range [−10, 10]^4, such that they induce bijective 3D digitized rotations. It is a comma-separated values file format such that each line contains a different quaternion. This is an updated version which contains 576 more quaternions with respect to the previous version. These 576 quaternions where previously certified as ones which do not lead to bijective digitized rotations due to a bug in the used implementation of the algorithm described in:</p> <p>Pluta K., Romon P., Kenmochi Y., Passat N. (2016) Bijectivity Certification of 3D Digitized Rotations. In: Bac A., Mari JL. (eds) Computational Topology in Image Context. CTIC 2016. Lecture Notes in Computer Science, vol 9667. Springer, pp 30-41, doi:10.1007/978-3-319-39441-1_4</p> <p> </p> <p><strong>Acknowledgements:</strong><br> Special thanks for Victor Ostromoukhov and David Cœurjolly of University of Lyon 1, LIRIS, France, for finding the bug.</p>
Digital models of test objects captured by RFSAT Ltd using 3D photogrammetry
<p>This data set contains a number of digital models produced via 3D photogrammetric scanning as part of the SCAN4RECO project, funded by the European Horizon'2020 program. Scanning and processing of models was done with Pix4D Mapper and Autodesk ReMake software from images captured with Canon 5DS camera in 50 Megapixel image resolution. Example objects include Byzantine icons painted on wood, oil paintings on canvas and painted Venetian carnival paper masks.</p> <p>Second version of the data set includes historical icons of Saint DImitrios and Saint Archangel Michael, an icon of Saint Mary painted specially for testing SCAN4RECO technologies, as well as models of an original high-relief sculpture from OPD and of its 3D printed copy (made by Fraunhofer-IGD and hand painted by RFSAT)..</p> <p>Selected models can be also seen in the SCAN4RECO Virtual Museum developed by CERTH-ITI:<br> http://scan4reco.eu/scan4reco/content/scan4reco-virtual-museum</p>
Dataset for BRDF representation in response to the build orientation in 3D-printed digital materials
<p>This dataset folder contains data for the project "BRDF representation in response to the build orientation in 3D-printed digital materials"<br> For more information please contact Ali Payami Golhin (payami.ag@gmail.com)</p> <p>Abbreviations:<br> C:Cyan; M:Magenta; Y:Yellow; K:Black<br> GoG: Glossy on Glossy finish; GoM: Glossy on Matte finish</p> <p>Folder "Color values": presents data for CIEXYZ, CIELab, CIELCh for 328 measurement geometries for each CMYK resins<br> Folder "Spectral data": presents reflctance data for 328 measurement geometries for each CMYK resins. The first column in each file represent wavelength (nm) and the second column contains spectral data<br> File "PCA.xlsx": presents PCA (PC1) scores for CMYK colors</p>
Lipschitz quaternions in the range [−10, 10]^4, which do not induce bijective 3D digitized rotations
<p>The file contains Lipschitz quaternions in the range [−10, 10]^4, such that they do not induce bijective 3D digitized rotations. For a list of the Lipschitz quaternions from the range which do induce bijective 3D digitized rotations, please, see : https://doi.org/10.5281/zenodo.814552 </p> <p>It is a comma-separated values file format such that each line contains a different quaternion. The quaternions were certified by the algorithm described in:</p> <p>Pluta K., Romon P., Kenmochi Y., Passat N. (2016) Bijectivity Certification of 3D Digitized Rotations. In: Bac A., Mari JL. (eds) Computational Topology in Image Context. CTIC 2016. Lecture Notes in Computer Science, vol 9667. Springer, pp 30-41, doi:10.1007/978-3-319-39441-1_4</p>
Data from: 3D printed digital pneumatic logic for the control of soft robotic actuators
<p>Soft robots are paving their way to catch up with the application range of metal-based machines and to occupy fields which are challenging for traditional machines. Pneumatic actuators play an important role in this development, allowing the construction of bioinspired motion systems. Pneumatic logic gates provide a powerful alternative for controlling pressure-activated soft robots, which are often controlled by metallic valves and electric circuits. Many existing approaches for fully compliant pneumatic control logic suffer from high manual effort and low pressure tolerance. In our work, we invented 3D printable, pneumatic logic gates that perform Boolean operations and imitate electric circuits. Within 7 hours, an FDM printer is able to produce a module that serves as either an OR, AND or NOT gate; the logic function is defined by the assigned input signals. The gate contains two alternately acting pneumatic valves, whose work principle is based on the interaction of pressurized chambers and a 3D printed 1 mm tube inside. The gate design does not require any kind of support material for its hollow parts, which makes the modules ready to use directly after printing. Depending on the chosen material, the modules can operate on a pressure supply between 80 and over 750 kPa. The capabilities of the invented gates were verified by implementing an electronics-free drink dispenser based on a pneumatic ring oscillator and a 1-bit memory. Their high compliance is demonstrated by driving a car over a fully flexible, 3D printed robotic walker controlled by an integrated circuit.</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 11. Online accessible repository of digital data on cultural heritage with X3D models (STARC Web Repository, 2017, © Copyright 2017, STARC, Cyprus Institute. Used with permission)
<p>Prototyping can also include the development of toolkits for automatic content generation simulator, but in the case of an architectural environment, the components are too complex to be automatically generated. Furniture elements or the learning artifacts (i.e. content created by learners) can be converted to be viewed in X3D compatible browsers or included in online galleries (Figure 11). After functional and 3D content prototyping, certain components of the virtual campus can be easily modified and adapted as needed.</p>
FIG. 31. Digital 3D in Cranial Morphology And Phylogenetic Relationships Of Trigonostylops Wortmani, An Eocene South American Native Ungulate
FIG. 31. Digital 3D reconstructions of left osseous labyrinths of Trigonostylops wortmani AMNH VP-28700 and Astrapotherium magnum MACN A 3208 in lateral, ventral, and dorsal views. Both specimens are damaged (area of fenestra vestibuli in AMNH VP-28700; fenestra cochleae in MACN A 3208: arrow).
Data from: 3D printed digital pneumatic logic for the control of soft robotic actuators
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Image Dataset for 'Digitally deconstructing leaves in 3D using X-ray microcomputed tomography and machine learning'
<p>Dataset used in the manuscript 'Digitally Deconstructing Leaves in 3D Using X-ray microcomputed Tomography and Machine Learning'. Please cite the paper presenting this dataset:</p> <p><strong>Citation:</strong> Théroux-Rancourt, G., M. R. Jenkins, C. R. Brodersen, A. McElrone, E. J. Forrestel, and J. M. Earles. 2020. Digitally deconstructing leaves in 3D using X-ray microcomputed tomography<strong> </strong>and machine learning. <em>Applications in Plant Sciences</em> 8(7): .</p> <p> </p> <p><strong>Description of the dataset</strong></p> <p>A 'Cabernet Sauvignon' grapevine (<em>Vitis vinifera</em> L.) leaf from a plant of the BOKU experimental vineyard in Tulln, Austria, was scanned using microCT at the Swiss Light Source. The original reconstructions of the scans are using the gridrec (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Gridrec_reconstruction_downsized.zip?versionId=28d98982-f69d-4eac-9dfa-efcc89c6823c">Gridrec_reconstruction_downsized.zip</a>) and the paganin, or phase-contrast, algortithm (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Phase_contrast_reconstruction_downsized.zip?versionId=bef3260d-2865-4c9b-b5e1-e692edefb691">Phase_contrast_reconstruction_downsized.zip</a>). To facilitate automated segmentation, the size of the image in the <em>x </em>and <em>y</em> dimensions have been halved, so that the size of the pixels is 0.325 µm in those dimensions, but 0.1625 µm in the <em>z</em> (slices) dimension.</p> <p>A binary image segmenting the leaf cells and the airspace for each gridrec and phase-contrast stacks are created, and both are combined together (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Binary_stack_for_local_thickness.zip?versionId=165e3938-b490-4e56-9c8e-a2084cb39d49">Binary_stack_for_local_thickness.zip</a>), a map of the local thickness is created (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Local_thickness_map.zip?versionId=ce0a7dc7-5e3f-44a4-8881-cf84b6efd87c">Local_thickness_map.zip</a>). This map gives information on the largest diameter of the pixels labeled as cells in the binary stack.</p> <p>Hand-labeled slices or ground truths were drawn on the following slices: 80, 140, 200, 260, 340, 400, 440, 540, 620, 740, 800, 860, 940, 1060, 1140, 1240, 1300, 1400, 1480, 1540, 1600, 1690, 1740, 1840 (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Hand_labelled_slices.tif?versionId=a21a13ac-fa47-4ef8-a903-ecc433787184">Hand_labelled_slices.tif</a>).</p> <p>Using the hand-labeled slices and the different images, a random-forest model was trained, which allowed to automatically segment the remaining slices of the stack (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Fullstack_Prediction_Example-6_training_slices-6_testing_slices.zip?versionId=02b69e65-da85-492e-9b72-9b2b3ccd085f">Fullstack_Prediction_Example-6_training_slices-6...</a>).</p> <p>The source code for the segmentation program is available <a href="https://github.com/plant-microct-tools/leaf-traits-microct/tree/master">here</a>, and the source code for the testing used in the paper is available <a href="https://github.com/plant-microct-tools/leaf-traits-microct/tree/nb-slices-eval">here</a>.</p>
Digital Twin of 3D Printer in Real Time Mode
<p>Digital Twin operating in real-time mode. (Please note that the window on the top right of the video is from Repetier (3D Printing Application) and it was added as a visual guide on what the 3D printer was printing. It was not part of the study.</p>
Data from: Diphthong Synthesis using the Dynamic 3D Digital Waveguide Mesh
<p>This dataset comprises the data and MATLAB scripts associated with the paper "Diphthong Synthesis using the Dynamic 3D Digital Waveguide Mesh" published in <em>IEEE/ACM Transactions on Audio, Speech and Language Processing</em>.</p> <p>The dataset contains:</p> <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> <p><strong>Contact: amelia.gully@york.ac.uk</strong></p>
Digital 3D Model Dataset from Thingiverse Data
<p>Digital model data as STL files, acquired from thingiverse by random probing of available things. Geometrical analysis and rendering as PNG and GIF performed. Pre-sliced machine instructions as GCode generated using slic3r. AMF files are also part of the dataset, these files were generated from the STL files.</p>
Audio from: 'Modeling Voiced Stop Consonants using the 3D Dynamic Digital Waveguide Mesh Vocal Tract Model'
<p>Audio files associated with the paper 'Modeling Voiced Stop Consonants using the 3D Dynamic Digital Waveguide Mesh Vocal Tract Model', presented at the International Congress of Phonetic Sciences 2019, Melbourne, Australia.</p>
A 3D Adult Zebrafish Brain Atlas (AZBA) for the Digital Age
<p>Zebrafish have made significant contributions to our understanding of the vertebrate brain and the neural basis of behavior, earning a place as one of the most widely used model organisms in neuroscience. Their appeal arises from the marriage of low cost, early life transparency, and ease of genetic manipulation with a behavioral repertoire that becomes more sophisticated as animals transition from larvae to adults. To further enhance the use of adult zebrafish, we created the first fully segmented three-dimensional digital <u>a</u>dult <u>z</u>ebrafish <u>b</u>rain <u>a</u>tlas (AZBA). AZBA was built by combining tissue clearing, light-sheet fluorescence microscopy, and three-dimensional image registration of nuclear and antibody stains. These images were used to guide segmentation of the atlas into over 200 neuroanatomical regions comprising the entirety of the adult zebrafish brain. As an open source, online (azba.wayne.edu), updatable digital resource, AZBA will significantly enhance the use of adult zebrafish in furthering our understanding of vertebrate brain function in both health and disease.</p>
Data obtained during classification of intertidal habitats using UAV imagery in the Galapagos Archipelago (Orthophotos, digital elevation models (DEM) and orthophoto-draped 3D models)
<p>In the repository 5 folders exist. 1) Digital elevation models (DEMs), 2) Intertidal habitat map, 3) Othophoto draped 3D models, 4) Orthophotos, and 5) Processing reports. The data has been collected in Puerto Ayora at Santa Cruz in August 2017, the most urbanized island of the Galapagos Archipelago. The purpose of this study was to investigate the image classification opportunities for these intertidal habitats using Uncrewed Aerial Vehicle (UAV) imagery. This dataset is cited in an open-access publication: https://doi.org/10.3390/drones7070416. </p>
A 3D Adult Zebrafish Brain Atlas (AZBA) for the Digital Age
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Data from: Digitizing extant bat diversity: an open-access repository of 3D μCT-scanned skulls for research and education
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Supplementary material 2 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
SV1: EDOF imaging : Explanation note: This video demonstrates the effect of the registered EDOF-calculation.
Supplementary material 1 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Technical information : Explanation note: Detailed technical information and additional theoretical background.
Supplementary material 3 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
SV2: Illustrative examples : Explanation note: Illustrative examples of insects and snail shell models generated with DISC3D.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.