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69 results for “visual modeling”
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>
Photogrammetry-based model of the Copacabana for auralization and audio-visual perception studies in virtual reality
<p>This dataset contains photogrammetry-based audio-visual models from Copacabana. They are used for urban sound auralization as well as for audio-visual perception studies in virtual reality.</p> <p>Photogrammetry model available in the following formats: 3ds, dae, dxf, fbx, obj, stl</p> <p>Simplified CAD model for acoustic simulations available in format: dae</p>
Datastes for NeuroDAVIS: A neural network model for data visualization
<p>These are the datasets used in the work NeuroDAVIS: A neural network model for data visualization.</p>
RoboFinch: a versatile audio-visual synchronized robotic bird model for laboratory and field research on songbirds
<p>Raw data and R script for the behavioral data published in the Journal Methods in Ecology and Evolution with the title: RoboFinch: a versatile audio-visual synchronized robotic bird model for laboratory and field research on songbirds</p> <p>All data and source files for the RoboFinch construction can be found here: https://doi.org/10.5281/zenodo.7520589</p> <p> </p>
Measures and models of visual acuity in epipelagic and mesopelagic teleosts and elasmobranchs
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Asymmetry in kinematic generalization between visual and passive lead-in movements are consistent with a forward model in the sensorimotor system
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Supplementary video content for "Three-dimensional visualization of ensemble weather forecasts", Parts 1 and 2 (Geoscientific Model Development, 2015)
<p>Supplementary video content (full resolution) for the papers "Three-dimensional visualization of ensemble weather forecasts - Part 1: The visualization tool Met.3D (version 1.0)" and "Three-dimensional visualization of ensemble weather forecasts - Part 2: Forecasting warm conveyor belt situations for aircraft-based field campaigns", Geoscientific Model Development, 2015. The corresponding papers can be found on http://geosci-model-dev.net/.</p>
Representations of language in a model of visually grounded speech signal: Data
<p>The set of datafiles to reproduce results from:</p> <ul> <li>Chrupała, G., Gelderloos, L., & Alishahi, A. (2017). Representations of language in a model of visually grounded speech signal. ACL. arXiv preprint: https://arxiv.org/abs/1702.01991</li> </ul>
Training and test data, plus saved models for the paper "Top-down effects in an early visual cortex inspired hierarchical Variational Autoencoder" submitted to the SVRHM 2022 Workshop @ NeurIPS
<p>Each .pkl file contains a training or test dataset in the form of a Python dictionary (generated with Python 3.8.5) with the following fields:</p><ul><li>'train_images': 640,000 float32 images used for model training. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each.</li><li>'train_labels': float32 labels for each image in 'train_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.</li><li>'test_images': 64,000 float32 images used for model testing. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each.</li><li>'test_labels': float32 labels for each image in 'test_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.</li></ul><p>Each .zip file contains a saved model. Details on these are coming soon.</p><p>For more details, see the paper "Top-down effects in an early visual cortex inspired hierarchical Variational Autoencoder" published at the SVRHM 2022 Workshop @ NeurIPS (<a href="https://openreview.net/forum?id=8dfboOQfYt3">link</a>).</p>
Data from: Color variation and visual modeling provide no support for adaptive coloration in a blue crayfish
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An actor-model framework for visual sensory encoding
<pre>This folder contains the dataset used in the article 'An actor-model framework for visual sensory encoding'. </pre> <pre>There are three main folders 'models' contains the trained neural network for the different experiments.<br>The code to load the model and generate the figures in the article is available<br>in https://github.com/lne-lab/actor-retina<br>or at https://doi.org/10.5281/zenodo.10519578 'spikes_data' contains the data used to train the neural network.<br>This can be used to train a network from scratch. 'Van_Hateren_image' contains the image dataset used in the experiments.<br><br>UPDATE (2025-08-22)<br>'stimulus' contains the stimulus used as input to train the forward and actor models.</pre>
Input files and movie visualizations for convection models discussed in Becker and Fuchs, "Generation of evolving plate boundaries and toroidal flow from visco-plastic damage-rheology mantle convection and continents", manuscript revised for G-Cubed
<p>These input files are for the CitcomS software as available on github.com/geodynamics/citcoms and used in the version under commit 2bda530. They can be used to recreate the models discussed in Becker and Fuchs (revised manuscript submitted to G-Cubed, 11/2023), with model codes discussed and listed in Table 1 of the preprint as provided here. We also provide selected animations of the time dependence of model output, referenced to the same model names.</p>
Stable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations: Data and Visualization Notebooks
<p>The data, jupyter notebooks, and saved model weights for the "Stable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations" Hu et al. (2025) arxiv preprint: <a href="https://arxiv.org/abs/2407.00124">arXiv:2407.00124</a>. This updated version contains more analysis notebooks together with related data/model.</p>
Visualizing reconstructed tree models
<p>Visualization on how a point cloud is created by a terrestrial laser scanner, and how a reconstructed quantitative structure model can be visualized in various ways. The visualization can be a cylinder model as shown in previous videos, but it can also be converted to a more continuous Bézier surface. The video also shows how the tree model can be augmented with non-intersecting leaves by sampling a certain distribution based on the branching structure. Both the branches and the leaves can be textured either for realism or something totally different.</p> <p>This animation was produced by the Inverse Problems research group in the Department of Mathematics at Tampere University of Technology (http://math.tut.fi/inversegroup).</p> <p>Animation created using Blender (http://www.blender.org).</p> <p>Music:<br> "Vanes" by Kevin MacLeod (http://incompetech.com)<br> Licensed under Creative Commons: By Attribution 3.0<br> http://creativecommons.org/licenses/by/3.0/</p> <p>Textures:<br> "Bark 0007"<br> xoio (xoio.de)</p> <p>"Cherry Leaf"<br> BrianHanson2nd (deviantart.com)<br> Licensed under Creative Commons: By Attribution 3.0<br> http://creativecommons.org/licenses/by/3.0/</p> <p>The animation builds upon but does not directly feature the "Prunus avium - Cherry Tree" point cloud data by Jan Hackenberg (http://www.simpletree.uni-freiburg.de/openData.html) shared under the Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International license<br> http://creativecommons.org/licenses/by-nc-sa/4.0/</p>
Supplementary Material: Effectiveness of Performance Visualizations for Declarative Model Transformations
<p>We performed a case study to evaluate whether our performance visualizations developed for the declarative transformation language Henshin are suitable for performing a root cause analysis. Our study consisted of four parts. 1) Participants completed a questionnaire that collected data on their demographics and knowledge of model transformations. 2) The participants watched a video explaining the basics of models, Henshin, and our performance visualizations. 3) The study participants solved four different tasks one after the other. Guided by a questionnaire, they carried out a root cause analysis. 4) Finally, in a short interview session, we asked the participants about their assessment of the comprehensibility and usefulness of the visualizations.</p> <p>In total, 18 participants took part in our study. Our results show that most participants could correctly read and interpret the information provided by the visualizations. The majority of our participants could propose a performance optimization based on the visualizations that optimized the execution of a transformation.</p> <p>This data set contains our study material, which is necessary to repeat the study, our raw and processed data.</p>
Data from: How biological attention mechanisms improve task performance in a large-scale visual system model
How does attentional modulation of neural activity enhance performance? Here we use a deep convolutional neural network as a large-scale model of the visual system to address this question. We model the feature similarity gain model of attention, in which attentional modulation is applied according to neural stimulus tuning. Using a variety of visual tasks, we show that neural modulations of the kind and magnitude observed experimentally lead to performance changes of the kind and magnitude observed experimentally. We find that, at earlier layers, attention applied according to tuning does not successfully propagate through the network, and has a weaker impact on performance than attention applied according to values computed for optimally modulating higher areas. This raises the question of whether biological attention might be applied at least in part to optimize function rather than strictly according to tuning. We suggest a simple experiment to distinguish these alternatives.
Study on a quantitative method for determining mixing proportion of transparent cemented soil for visual geotechnical model tests
<p>An effective mixing of transparent cemented soil is necessary for visual geotechnical model tests, so a quantitative method for determining mixing proportion of transparent cemented soil was generated in this paper. Firstly, quartz sand, Nanoscale silica powder and N-dodecane mixed 15# white oil were selected as the raw materials, and a series of orthogonal experiments were designed. Concurrently, the main physical and mechanical parameters (volumetric weight γ, internal friction angle φ, cohesion c) of transparent cemented soil were measured, caused by the change of "particle size of quartz sand" and " mass ratios between fumed silica and fused quartz". Subsequently, multiple linear regression equations of various physical and mechanical parameters (γ, φ, c) were obtained by fitting the original test data. Finally, the rationality of multiple linear regression equations was proved. The research results indicated: (1) the volumetric weight changes from 16.13kN/m<sup>3</sup> to 12.53kN/m<sup>3</sup>, the Internal friction angle is between 27.07° and 14.82°, and the cohesion varies from 31kPa to 2.3kPa, the parameters meet the similar requirements of the surrounding rock (grade ⅳ and ⅴ) and clay; (2) The values of Multiple R values (all greater than 0.88) and the Significance F value (all close to 0) proves the three regression equations were valid; (3) Combining the three regression equations and particle size of quartz sand, the mass ratio between fumed silica and fused quartz and geometry similarity constant were solved. All the conclusions mentioned could provide theoretical support and data reference for transparent soil model test implementation.</p>
Data from: Integrating passive acoustic and visual data to model spatial patterns of occurrence in coastal dolphins
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Study on a quantitative method for determining mixing proportion of transparent cemented soil for visual geotechnical model tests
Open the record for dataset details and reuse information.
Data from: How biological attention mechanisms improve task performance in a large-scale visual system model
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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.