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49 results for “3D Visualization”
Hand-selective visual regions represent how to grasp 3D tools for use: brain decoding during real actions
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3D-rhi-synth-2000- Synthetic Rhinophyma Visual Dataset
<p>In the real world, only a handful of data is available for the Rhinophyma skin condition, typically numbering in the hundreds. This repository contains a Synthetic Dataset of Rhinophyma, generated through 3D head models of one male and one female. The purpose of this data generation is to address the data scarcity of the Rhinophyma skin condition within the medical visual data and computer vision community. By generating such data, we aim to bridge the gap in data scarcity for this disease condition, as well as introduce a proof-of-concept methodology for generating synthetic data for specialized disease conditions.</p> <p>The <code>highlight</code> folder 'highlight_female_male_rendered' provides a glimpse of the entire dataset. The file <code>'2000_deformations.npy</code>' contains the 2000 values of deformations applied during rendering.</p> <p>The dataset is divided into two main folders: 'female_rendered' and 'male_rendered'. Within each of these folders, there are three subfolders: 'configu', 'images', and 'points.</p> <p>1. 'configu': This subfolder contains `.json` files with configuration details for each model. The files include various parameters, such as:<br> - "total_num_cameras": the total number of cameras.<br> - "active_camera_name": the name of the active camera.<br> - "camera_focal_len": the camera's focal length.<br> - "camera_loc": the camera's location.<br> - "camera_rot": the camera's rotation.<br> - "nose_deformation_severity": a measure of the severity of nose deformation.<br> - "label": the label for the model (e.g., "Severe").<br> - "nose_variants": additional details about nose variants.</p> <p>2. 'images': This subfolder contains the rendered images in resolution 960x540. They are named according to the following convention e.g.'Nose_Deformation_Severity_0_2.716669764843742_Camera_00001', with specific details related to the deformation severity and camera number. There are images for 10 different cameras.</p> <p>3. 'points': This subfolder contains polygon files corresponding to each model. These files represent the deformations applied to the models during rendering.</p> <p>|-- Dataset Root<br> |-- female_rendered<br> | |-- configu<br> | | |-- Nose_Deformation_Severity_0_2.212930927821943_Camera_00001.json<br> | | |-- Nose_Deformation_Severity_0_2.212930927821943_Camera_00002.json<br> | | |-- ...<br> | |<br> | |-- images<br> | | |-- Nose_Deformation_Severity_0_2.212930927821943_Camera_00001.png<br> | | |-- Nose_Deformation_Severity_0_2.212930927821943_Camera_00002.png<br> | | |-- ...<br> | |<br> | |-- points<br> | | |-- Nose_Deformation_Severity_0_2.212930927821943.ply<br> | | |-- ...<br> | <br> |-- male_rendered<br> |-- configu<br> | |-- Nose_Deformation_Severity_0_2.716669764843742_Camera_00001.json<br> | |-- Nose_Deformation_Severity_0_2.716669764843742_Camera_00002.json<br> | |-- ...<br> |<br> |-- images<br> | |-- Nose_Deformation_Severity_0_2.716669764843742_Camera_00001.jpg<br> | |-- Nose_Deformation_Severity_0_2.716669764843742_Camera_00002.jpg<br> | |-- ...<br> |<br> |-- points<br> |-- Nose_Deformation_Severity_0_2.716669764843742.ply<br> |-- ...</p> <p>Together, these folders and files comprise a dataset designed to represent and analyze the Rhinophyma condition in both male and female 3D head models that we have created. These 3D models will be made available upon a genuine request to the authors of this dataset.</p>
3D visualization of bioerosion in archaeological bone
<p>Set of five microCT volume images of archaeological samples. 8-bit TIFF images stacks in zipped folders.</p>
3D printed map for blind or visually impaired people
<p>This data set is composed of three parts each having its proper origins, formats and rights. This data set was used to apply the methods of relief editing and image processing to facilitate the production of accessible documentation by having in hand an easy to use interface.</p>
Advancing Vanadium Redox Flow Battery Analysis: A Deep Learning Framework for High-Throughput 3D Visualization and Bubble Quantification via Synchrotron X-ray Tomography
<p>Dataset and model of UTILE-Redox - Deep Learning based Tool for Autonomous 3D Bubble Analysis of Vanadium Flow Batteries from Synchrotron X-ray Imaging. This project focuses on the deep learning-based automatic analysis of Vanadium Redox Flow Batteries (VRFB) Synchrotron X-ray tomographies. This repository contains the Python implementation of the UTILE-Redox software for automatic volume analysis, feature extraction, and visualization of the results.</p>
[Raw data] Media Coverage of 3D Visual Tools Used in Urban Participatory Planning
<p>Raw information on the articles used for the publication: Media Coverage of 3D Visual Tools Used in Urban Participatory Planning</p>
Supplementary data for the paper "Visual integration of omics data to improve 3D models of fungal chromosomes"
<ul> <li>13 parameter files (*.YML) used by the 3DGB workflow to produce models of 3D genomes.</li> <li>13 3D genomes structures (*.PDB).</li> <li>4 animated GIF of representative structures.</li> <li>1 XLSX file that lists raw (Hi-C and ChIP-seq) data used in this study and the associated analysis.</li> </ul>
Synthetic data set "Synth1" for the paper "Adaptive Sampling of 3D Spatial Correlations for Focus+Context Visualization"
<p>Synthetic data set "Synth1" for the paper "Adaptive Sampling of 3D Spatial Correlations for Focus+Context Visualization".<br>Preprint of the paper available at: <a href="https://arxiv.org/abs/2309.03308">https://arxiv.org/abs/2309.03308</a></p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 10. Room model generated with Autodesk 123D Catch - the 3D model (screen capture from GLC Player)
<p>Structure from motion was used for rapid modeling of a small room with all its objects. Two files were generated, a Wavefront obj and mtl (corresponding to the texture). The 3D model was post-processed with MeshLab, during which several filters were applied to clean up the model. The mesh model was also connected with the scanned model, by choosing at least 4 connection points. The 2D and 3D results are shown in Figures 9, 10. A post-processing could also be performed using the Autodesk 123D Catch web application.</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 2. The model of the 3D virtual campus - details from the building interior (3D modeling by Marius Hodea)
<p>The processing workflow for 3D modeling and design for a 3DVLE represents a time- consuming stage in the overall pipeline production. One reason is that a range of technologies and tools are typically used. In (Cudworth 2014) a 3-week period is indicated for experienced users to perform the 3D modeling of a virtual space. In our case, a 3-month work was needed for designing a working model of a 3D virtual campus (see Figure 1 and Figure 2 for final results).</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 1. The model of the 3D virtual campus - an outdoor view (3D modeling by Marius Hodea)
<p>The processing workflow for 3D modeling and design for a 3DVLE represents a time- consuming stage in the overall pipeline production. One reason is that a range of technologies and tools are typically used. In (Cudworth 2014) a 3-week period is indicated for experienced users to perform the 3D modeling of a virtual space. In our case, a 3-month work was needed for designing a working model of a 3D virtual campus (see Figure 1 and Figure 2 for final results).</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 9. Room model generated with Autodesk 123D Catch - the 2D model
<p>Structure from motion was used for rapid modeling of a small room with all its objects. Two files were generated, a Wavefront obj and mtl (corresponding to the texture). The 3D model was post-processed with MeshLab, during which several filters were applied to clean up the model. The mesh model was also connected with the scanned model, by choosing at least 4 connection points. The 2D and 3D results are shown in Figures 9, 10. A post-processing could also be performed using the Autodesk 123D Catch web application.</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 8. The 3D model of the faculty building in a HTML page
<p>The pipeline processing was the following: a) the 3D model from Sketchup was saved as a Collada file; b) this file has been imported in MeshLab (MESHLAB 2017) and converted to VRML97 format (wrl); c) aopt utility was used to convert wrl files to X3D and HTML5 files. The model was visualized in the OpenSim virtual world setting using an external browser (Figure 8).</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 5. Iteration of the 3D modeling of the virtual faculty building (3D modeling by Marius Hodea)
<p>For our research, several iterations and methods were employed for the 3D design of an online campus. Different virtual models of a faculty building (see Figures 4,5) were designed and finally a virtual model of a 3D campus comprising a simplified 3-story faculty building (Figure 6) was created. The objective was the optimization of the 3D model and the demonstration of the desired functionalities. For these purposes two 3D modeling and post-processing software were used, i.e. 3DSMax and Trimble Sketchup. The model of the building resulted in 5962 vertices and 4528 faces. The textures and illumination were applied using OpenSim’s in-world tools. Furniture objects (tables, chair, computer monitors) were taken from the Google 3D Warehouse, distributed and shared under Trimble General Model License.</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 12. Diagram of the proposed working methodology
<p>The chart below (Figure 12) summarizes the workflow recommended for the implementation of a prototype of a 3D online campus.</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>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 3. The graphic stack of X3DOM (Havele 2011)
<p>The current release of X3DOM supports native implementations (iOS8, Chrome, and Firefox for Android), with fallback to WebGL API, and partially to X3D/SAI plugins (INSTANTREALITY 2017). X3DOM is above WebGL, OpenGL and DirectX, and subsequently has less complexity (in Figure 3 is shown the graphical stack). Integrated into the HTML DOM, X3DOM allows web programmers to continue their experience, based on known web technologies such as CSS, Java Script, JQuery or Ajax. Standard technologies can streamline a VR or AR application development, by hiding the low-level complex tasks, and allow the access to device sensors and video camera via high-level API functions. X3DOM supports embedded X3D-XML files references using inline nodes, i.e. an X3D- XML file can reference other X3D-XML files and build a hierarchy of assets (X3DOM 2017) which can be loaded in the background with a higher throughput.</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 6. The first implementation of the virtual campus (OpenSim import of the 3D model)
<p>For our research, several iterations and methods were employed for the 3D design of an online campus. Different virtual models of a faculty building (see Figures 4,5) were designed and finally a virtual model of a 3D campus comprising a simplified 3-story faculty building (Figure 6) was created. The objective was the optimization of the 3D model and the demonstration of the desired functionalities. For these purposes two 3D modeling and post-processing software were used, i.e. 3DSMax and Trimble Sketchup. The model of the building resulted in 5962 vertices and 4528 faces. The textures and illumination were applied using OpenSim’s in-world tools. Furniture objects (tables, chair, computer monitors) were taken from the Google 3D Warehouse, distributed and shared under Trimble General Model License.</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 7. The HTML source (partial) code, integrating the X3D model
<p>To integrate the model into a web page, a model conversion to X3D format and an X3DOM output under the form of an HTML5 encoded webpage (Figure 7) were needed. Instant Reality distribution provides a command line transcoding tool, named Avalon Optimizer (aopt), that was used to convert a VRML format (wrl extension) of the model to X3D. </p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 4. Iteration of the 3D modeling of the virtual faculty building (3D modeling by Marius Hodea)
<p>For our research, several iterations and methods were employed for the 3D design of an online campus. Different virtual models of a faculty building (see Figures 4,5) were designed and finally a virtual model of a 3D campus comprising a simplified 3-story faculty building (Figure 6) was created. The objective was the optimization of the 3D model and the demonstration of the desired functionalities. For these purposes two 3D modeling and post-processing software were used, i.e. 3DSMax and Trimble Sketchup. The model of the building resulted in 5962 vertices and 4528 faces. The textures and illumination were applied using OpenSim’s in-world tools. Furniture objects (tables, chair, computer monitors) were taken from the Google 3D Warehouse, distributed and shared under Trimble General Model License.</p>
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.