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236 results for “rendering”
Experimental data for the motor learning study performed: "Towards functional robotic training: Motor learning of dynamic tasks is enhanced by haptic rendering but hampered by robotic assistance"
<p>The dataset contains the kinematic data and the questionnaire responses for a robot-assisted motor learning study performed in the Motor Learning and Neurorehabilitation Laboratory at the University of Bern. The details of the study are described in [doi: ]. The kinematic data for each participant is stored as a data frame inside a “pickle” (serialized python object) file. The questionnaire responses and population metrics are stored as “CSV” files. The variables inside the files are explained in “DataframeVariableDescription.rtf”. For questions, please contact oezhan.oezen@artorg.unibe.ch or L.MarchalCrespo@tudelft.nl.</p>
Experimental study dataset: "Enhancing Touch Sensibility by Sensory Retraining in a Sensory Discrimination Task via Haptic Rendering"
<p>Experimental study dataset: "Enhancing Touch Sensibility by Sensory Retraining in a Sensory Discrimination Task via Haptic Rendering."</p> <p>This research was supported by the Swiss National Science Foundation through the grant PP00P2 163800. This work was also supported by SENACYT and IFARHU, the Panamanian Government.</p> <p>Files and data to upload:</p> <ol> <li>Description of variables </li> <li>Continue robot data </li> <li>Discontinue robot data </li> <li>Questionnaire data </li> </ol> <p> </p>
Associated dataset for "Evaluation of Sensor Self-Noise in Binaural Rendering of Spherical Microphone Array Signals"
<p>The conducted instrumental and perceptual evaluation utilize the Real-Time Spherical Microphone Renderer (<a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR">ReTiSAR</a>) for binaural reproduction in Python. However, the provided execution configurations (see below) are probably not exactly in accordance with the latest ReTiSAR code base. Hence, the at the time employed code state should be used in order to exactly reproduce the rendering results in this data set. The frozen code state for this data set is available at:<br> <a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2020.ICASSP">https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2020.ICASSP</a></p> <p>Download the rendering pipeline and follow the setup instructions! Use the here included Conda environment file when setting up the Python environment. In this way you should obtain exactly the same Python setup as utilized in the instrumental and perceptual evaluation in the publication:</p> <pre><code>conda env create --file ReTiSAR_environment_freeze.yml</code></pre> <pre><code>source activate ReTiSAR_ICASSP_freeze</code></pre> <p>Directory "SNR":</p> <ul> <li>Tools for instrumental evaluation (Section 4)</li> <li>Shell script to capture input and output signals of rendering pipeline for sound field (target / wanted) and self-noise (unwanted) components for all specified configurations</li> <li>Matlab script to analyse captured signal and generate system transfer plots (Figure 1 to Figure 3 and further configurations)</li> </ul> <p>Directory "Relative Output Levels":</p> <ul> <li>Tools for preparation of perceptual evaluation (Section 5)</li> <li>Shell script to capture rendered uniformly contributing noise signals for all specified configurations</li> <li>Matlab script to analyse and level align captured signals and generate plot result plot (Figure 4)</li> </ul> <p>Directory "Absolute Output Levels":</p> <ul> <li>Tools for specification of perceptual evaluation (Section 5)</li> <li>Shell script to capture reproduced uniformly contributing noise signals for all specified configurations</li> <li>Matlab script to analyse the calibrated captured signals yielding the average level in the ear signals of 58.2 dBSPL (Section 5.1)</li> </ul> <p>Files in base directory and directory "Study Results":</p> <ul> <li>Tools for perceptual evaluation / user study (Section 5)</li> <li>Matlab GUI to conduct perceptual user study (employ by executing "ICASSP_gui.m", respective ReTiSAR instances are started and remote controlled by the GUI, raw study results will be stored in "results" directory)</li> <li>Matlab script to "calculate_conclusion.m" to analyse the raw study results and generate individual and conclusive result plots (Figure 5, Figure 6 and more)</li> </ul>
Elevation Models for Reproducible Evaluation of Terrain Representation - Inventory of Renderings
<p>This is an inventory of 155 renderings from 78 publications on terrain visualization techniques. The renderings guided the selection of landform types in the elevation models that are proposed in the following article:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p> <p>Visualization techniques include colored aspect, contour lines, hypsometric tints, plan oblique relief, relief shading, rock and scree representation, and spot heights. The inventory contains information about display scale of the sample renderings, landform types, cell size, and geographic location of the digital elevation models. Also inventoried are how authors evaluated their renderings, how scale was indicated on the renderings, and whether the cell size and source of elevation data was included.</p> <p>The inventory is formatted as a single table in CSV UTF-8 and MS Excel .xlsx formats. Papers are grouped by visualization type; attributes of each rendering are stored on a single row.</p>
Supplemental Data from the article "The SmARTR pipeline: a modular workflow for the cinematic rendering of 3D scientific imaging data"
<h1><strong>Please, refer to <a href="https://github.com/MeVisLab/SmARTR-Networks">this GitHub repository</a> for additional info, updates, issue reports, and discussion<br></strong></h1> <p><strong>A collection of configuration files (SmARTR networks) published in "<a href="https://doi.org/10.1016/j.isci.2024.111475">The SmARTR Pipeline: a modular workflow for the cinematic rendering of 3D scientific imaging data</a>", enabling the creation of cinematic (photorealistic) renderings of 3D data in the FREE software <a href="https://www.mevislab.de/download">MeVisLab</a><br></strong></p> <ul> <li>Each folder in the archive contains one or more SmARTR network files, the scan and mask files required for the practical examples detailed in the <a href="https://www.cell.com/cms/10.1016/j.isci.2024.111475/attachment/8d79036b-acb6-4cda-a5ff-f56317691ebc/mmc1.pdf">Supplemental Data</a> of the article, and an additional folder with LUT presets.</li> </ul>
Dataset of Spatial Room Impulse Responses in a Variable Acoustics Room for Six Degrees-of-Freedom Rendering and Analysis
<p>Room acoustics measurements are used in many areas of audio research, from physical acoustics modelling and speech enhancement to virtual reality applications. This paper documents the technical specifications and choices made in the measurement of a dataset of spatial room impulse responses (SRIRs) in a variable acoustics room. Two spherical microphone arrays are used: the mh Acoustics Eigenmike em32 and the Zylia ZM-1, capable of up to fourth- and third-order Ambisonic capture, respectively. The dataset consists of three source and seven receiver positions, repeated with five configurations of the room's acoustics with varying levels of reverberation. Possible applications of the dataset include six degrees-of-freedom (6DoF) analysis and rendering, SRIR interpolation methods, and spatial dereverberation techniques. </p> <p>Accompanying paper on details of the dataset measurement: https://arxiv.org/abs/2111.11882</p> <p>Changelog:</p> <p>V 1.0 - Initial version.<br> V 1.1 - SOFA files updated to latest Matlab API (1.1.3), 'SingleRoomDRIR' convention, with SourcePosition and ListenerPosition z data corrected. Changed ListenerPosition and SourcePosition x data so that it follows the convention of origin in bottom left corner (rather than the previous bottom right). Fixed the swapped x and y labels in 6dof_source_and_receiver_positions.pdf.</p>
Compiled version of UniStuttgart-VISUS/tpeqd-rendered-transitions with rendered videos
<p>The compiled static files and the generated data and video for the UniStuttgart-VISUS/tpeqd-rendered-transitions (<a href="https://github.com/UniStuttgart-VISUS/tpeqd-rendered-transitions" target="_blank" rel="noopener">GitHub repository</a>) prototype. The data.zip file needs to be extracted first, such that the data/ directory is placed in the same directory as the other files. The prototype should be viewed in a web browser other than Firefox for now (May 2024) to use the requestVideoFrameCallback API.</p>
Measuring Web Latency and Rendering Performance: Method, Tools & Longitudinal Dataset
<p>The dataset used in the paper entitled "Measuring Web Latency and Rendering Performance: Method, Tools & Longitudinal Dataset" published in IEEE Transactions for Network and Service Management. </p>
Piprahwa (Siddharthnagar district, Uttar Pradesh). Photographic rendering of reliquary inscription by India Museum, Calcutta.
<p>Piprahwa (Siddharthnagar district, Uttar Pradesh). Photographic rendering of reliquary inscription by India Museum, Calcutta.</p>
Piprahwa (Siddharthnagar district, Uttar Pradesh). Rendering of reliquary inscription, from museum image.
<p>Piprahwa (Siddharthnagar district, Uttar Pradesh). Rendering of reliquary inscription, from museum image.</p>
TauBench: A Dynamic Benchmark for Graphics Rendering (Dataset Reference Frames)
<p>TauBench is a dynamic graphics rendering benchmark dataset, targeted especially towards rendering methods relying on the reuse of temporal data. The dataset is available at <a href="https://doi.org/10.5281/zenodo.5729573">https://doi.org/10.5281/zenodo.5729573</a>, and this upload provides path traced reference frames for it in PNG format. The images are rendered with <a href="https://github.com/vga-group/tauray">Tauray</a> at 16384 samples per pixel (spp), at both 1080p and 2160p resolutions. Frame indices start from 0 and are <em>not</em> padded with leading zeroes.</p> <p>More information about TauBench is also available at <a href="https://webpages.tuni.fi/vga/taubench">https://webpages.tuni.fi/vga/taubench</a>.</p>
Test Scene Dataset for Physically Based Rendering
<p>A test scene data set for physically based rendering.<br> The scenes include different geometries, materials and illumination setups of varying complexity to facilitate the evaluation and testing of Monte-Carlo rendering algorithms.<br> The source files are provided in the Blender file format for easy editing and additional exports to the Mitsuba XML format are included.</p>
Supplementary Material—Evaluating tactile feedback in addition to kinesthetic feedback for haptic shape rendering: a pilot study
<p>Supplementary Material of the journal article: "<a href="https://www.frontiersin.org/articles/10.3389/frobt.2024.1298537/abstract">Evaluating tactile feedback in addition to kinesthetic feedback for Haptic Shape rendering: a pilot study</a>": DOI: <a href="https://doi.org/10.3389/frobt.2024.1298537">10.3389/frobt.2024.1298537</a></p> <p> </p>
Night Photography Rendering Challenge 2024 Dataset
<p>The procedure of capturing and processing images taken by cameras involves employing onboard processing to transform raw sensor images into the final, polished photographs, subsequently encoding them in a standard color space like sRGB.</p> <p>However, capturing images at night presents distinct challenges not typically encountered in daylight shots. Unlike daytime images, where assuming a single global illumination is often adequate, night images frequently feature multiple illuminants, many of which are visible in the scene. This complexity makes it difficult to determine the optimal illumination correction for rendering night images. Additionally, commonly used tone curves and photo-finishing techniques for daytime images may not be suitable for night photography. Furthermore, widely employed image metrics like SSIM and LPIPS may not effectively evaluate night images. The absence of established best practices and limited research in the realm of night photography pose significant challenges.</p> <p>The main objective of addressing this challenge is to stimulate research and advance the field of image processing specifically tailored for night photography.</p> <p>The participants in this challenge will be granted access to raw-RGB images of night scenes, which have been captured using the <strong>Huawei Mate 40 Pro</strong> sensor type and are encoded in 16-bit PNG files. Accompanying these images will be additional meta-data, provided in JSON files. As an added resource, the organizers have also provided code for a baseline algorithm, as well as a demonstration, on <a href="https://github.com/createcolor/nightimaging24">GitHub</a>.</p> <p>Dataset includes data postprocessed by baseline algorithm and resulting images processed by competitors` algorithms.</p> <p>Mean opinion scores are obtained through visual comparison, carried out using Yandex Tasks (similar to Mechanical Turk). Yandex Tasks users ranked the solutions in a forced-choice manner. It is important to note that Yandex Tasks primarily relies on observers from Eastern Europe and and Central Asia to perform the image ranking, and as a result, there may be a cultural bias in terms of the preferred image aesthetics by the observers. Yandex Tasks users were not aware of the identity of the participants. An example of this evaluation process can be found at a <a href="https://nightimaging.org/challenges/2022/ranking/index.html">specified link</a>.</p>
How to render species comparable taxonomic units through deep time: A case study on intraspecific osteological variability in extant and extinct lacertid lizards
<p>Generally, the species is considered to be the only naturally occurring taxon. However, species recognized and defined using different species delimitation criteria cannot readily be compared, impacting studies of biodiversity through Deep Time. This comparability issue is particularly marked when comparing extant with extinct species because the only available data for species delimitation in fossils are derived from their preserved morphology, which is generally restricted to osteology in vertebrates. Here, we quantify intraspecific, intrageneric, and intergeneric osteological variability in extant species of lacertid lizards using pairwise dissimilarity scores based on a data set of 253 discrete osteological characters for 99 specimens referred to 24 species. Variability is always significantly lower intraspecifically than between individuals belonging to distinct species of a single genus, which is in turn significantly lower than intergeneric variability. Average values of intraspecific variability and associated standard deviations are consistent (with few exceptions), with an overall average within a species of 0.208 changes per character scored. Application of the same methods to six extinct lacertid species (represented by 40 fossil specimens) revealed that intraspecific osteological variability is inconsistent, which can at least in part be attributed to different researchers having unequal expectations of the skeletal dissimilarity within species units. Such a divergent interpretation of intraspecific and interspecific variability among extant and extinct species reinforces the incomparability of the species unit. Lacertidae is an example where extant species recognized and defined based on a number of delimitation criteria show comparable and consistent intraspecific osteological variability. Here, as well as in equivalent cases, application of those skeletal dissimilarity values to paleontological species delimitation potentially provides a way to ameliorate inconsistencies created by the use of morphology to define species.</p>
Multi-view rendered YCB dataset for mobile manipulation
<p>This dataset contains different scenarios wherein a mobile robot is approaching a set of YCB objects using both its base and arm motions. There are a total of sixteen sequences with around 100-time steps per sequence. All the sequences were generated using BlenderProc photo-realistic renderer (https://github.com/DLR-RM/BlenderProc). Eight different YCB objects were used. All these objects have a unique 6D pose, while some of the objects also have a single or multiple axes of symmetry. In each sequence maximum of three objects were randomly sampled. In addition, for each sequence, there are five views of the objects from external cameras placed between 2.5-3-5 m facing towards the objects. </p> <p>This dataset was used for experimental evaluation in the following<strong> </strong>ICRA 2022 paper:</p> <p><em><strong>Naik, L., Iversen, T. M., Kramberger, A., Wilm, J., & Krüger, N. (Accepted/In press). Multi-view object pose distribution tracking for pre-grasp planning on mobile robots. In 2022 IEEE International Conference on Robotics and Automation (ICRA) IEEE.</strong></em></p> <p> </p> <p><strong>Technical details:</strong></p> <p>In each sequence, the first 5 frames (0-4) contain views from external cameras while frames (5-104) provides a view of the objects visible in the robot camera as it approaches the objects. All the ground truths are provided using the 'coco' annotations format.</p>
Various PLD renderings of a Carausius Antoninianus coin (Zuid-Beveland, NED)
<p>Renderings of 4 variants based on one Portable Light Dome (by KU Leuven) image dataset of both sides of the coin.</p> <p>Line 1: Standard visual representation with the recorded texture, any reflection with highlights are removed by the photometric stereo algorithm.<br> Line 2: Representation without the recorded texture, a so-called shaded image. Only the surface geometry and the shading of a particular selected virtual light setting casted on the surface are visualized.<br> Line 3: Normal maps, this 2D representation reveals 3D information. Every pixel in the image reveals information in what direction that point on the original surface is geometrically oriented. Pixels with blue shades are oriented towards the camera, the green shades towards the left upper corner, the red shades towards the right lower corner (Based on this information the PLD system can generate 3D models of the imaged surfaces, see line 4).<br> Line 4: Orthophoto of a 3D rendering of the PLD image dataset. On the 3D models a radiance scaling filter differentiates and accentuates the relief features. A particular virtual light casts light on the surface dropping shadows. </p>
Wikipedia rendered as synthetic handwriting
<p>This is the synthetic handwriting data used to pre-train <strong>Dessurt</strong> (<a href="https://arxiv.org/abs/2203.16618">https://arxiv.org/abs/2203.16618</a>).</p> <p>It is text sampled from Wikipedia and generated with the method described in "Text and Style Conditioned GAN for Generation of Offline Handwriting Lines" (<a href="https://arxiv.org/abs/2009.00678">https://arxiv.org/abs/2009.00678</a>). More data can be quite easily obtained using this code: <a href="https://github.com/herobd/handwriting_line_generation">https://github.com/herobd/handwriting_line_generation</a></p> <p>Inside the tar is a single directory with ~800k generated handwriting line images ("sample_0.png", "sample_123.png", "sample_3292524.png", etc.), and "OUT.txt" which has the GT for each line image.</p>
Supplementary material S2. Leiestes tomaszewskae sp. nov., holotype, Nr. 6723 [MAIG], X-ray micro-CT volume rendering of the habitus (without legs).
<p>X-ray micro-CT volume rendering of the habitus (without legs) of <em>Leiestes</em> <em>tomaszewskae</em> sp. nov., holotype, Nr. 6723 [MAIG].</p>
Supplementary material S1. Leiestes tomaszewskae sp. nov., holotype, Nr. 6723 [MAIG], X-ray micro-CT volume rendering of the habitus.
<p>X-ray micro-CT volume rendering of the habitus of <em>Leiestes</em> <em>tomaszewskae</em> sp. nov., holotype, Nr. 6723 [MAIG].</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.