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6 results for “materials and textures”
VR-Together Pilot 3: Geometry of living room, bedroom. Materials, textures and illumination
<p>VR-Together Pilot 3 geometry of living room and bedroom, materials, textures and illumination.</p> <p>This dataset contains de material created for the <a href="https://vrtogether.eu/about-vr-together/pilots/pilot3/">Pilot 3 of the VR-Together project</a>. In particular, it contains the following material:</p> <ul> <li>Geometry of the living room</li> <li>Geometry of the bedroom</li> <li>Objects</li> <li>Textures</li> <li>Illumination scheme</li> </ul> <p>All the previous material has been created in Unity.</p> <p>VR-Together has been funded by the European Commission as part of the H2020 program, under the grant agreement 762111.</p>
MatSim Dataset and benchmark for one-shot visual materials and textures recognition
<p><strong>The MatSim Dataset and benchmark</strong></p> <p>Synthetic dataset and real images benchmark for visual similarity recognition of materials and textures.</p> <p>MatSim: a synthetic dataset, a benchmark, and a method for computer vision-based recognition of similarities and transitions between materials and textures focusing on identifying any material under any conditions using one or a few examples (one-shot learning).</p> <p>Based on the paper: <a href="https://arxiv.org/pdf/2212.00648.pdf">One-shot recognition of any material anywhere using contrastive learning with physics-based rendering</a></p> <p> </p> <p><strong>Benchmark_MATSIM.zip: </strong>contain the benchmark made of real-world images as described in the paper</p> <p><strong>Dataset Generation Scripts.zip: </strong>Contain the Blender (4.1) Python scripts used for generating the dataset<br><br><a href="https://zenodo.org/record/7390166/files/MatSim_object_train_split_1.zip?download=1"><strong>MatSim_object_train_split_1,2,3....zip:</strong> </a>Contain a subset of the synthetics dataset for images of CGI images materials on random objects as described in the paper.</p> <p><strong>MatSimTrainObjectsNearField_.zip </strong>Contain train sets with near fieldlight sources</p> <p><strong><a href="https://zenodo.org/record/7390166/files/MatSim_Vessels_Train_1.zip?download=1">MatSim_Vessels_Train_1,2,3....zip </a></strong><a href="https://zenodo.org/api/files/020f90b2-7c41-44ad-86e3-69257884a569/MatSim_object_train_split_1.zip"><strong>:</strong> </a>Contain a subset of the synthetics dataset for images of CGI images materials inside transparent containers as described in the paper.<br><br><strong>*Note: these are subsets of the dataset; the full dataset can be found at:</strong><br><a href="https://e1.pcloud.link/publink/show?code=kZIiSQZCYU5M4HOvnQykql9jxF4h0KiC5MX">https://e1.pcloud.link/publink/show?code=kZIiSQZCYU5M4HOvnQykql9jxF4h0KiC5MX</a></p> <p>or<br><a href="https://icedrive.net/s/A13FWzZ8V2aP9T4ufGQ1N3fBZxDF">https://icedrive.net/s/A13FWzZ8V2aP9T4ufGQ1N3fBZxDF</a></p> <p> </p>
Supplementary material to: "Formation of low-pressure reaction textures during near-isothermal exhumation of hot orogenic crust (Bohemian Massif, Austria)"
<p>Supplemantary material to "Sorger, D., Hauzenberger, C. A., Finger, F., Linner, M., Skrzypek, E., & Schorn, S. (2024). Formation of low-pressure reaction textures during near-isothermal exhumation of hot orogenic crust (Bohemian Massif, Austria). Journal of Metamorphic Geology, 42(1), 3–34."</p>
VasTexture: Vast repository of textures and PBR Materials extracted from images using unsupervised approach
<h2><strong>VasTexture: Vast repository of textures and SVBRDF/PBR Materials extracted from images using an unsupervised approach.</strong></h2> <p> </p> <p>This dataset contains hundreds of thousands of textures and PBR/SV-BRDF materials extracted from real-world natural images.</p> <p> </p> <p>The repository is composed of RGB images of textures given as RGB images (each image is one uniform texture) and folders of PBR/SVBRDF materials given as a set of property maps (base color, roughness, metallic, etc).</p> <p>Note that this contain subset of repository more could be found in the <a href="https://sites.google.com/view/infinitexture/home" target="_blank" rel="noopener">main project page</a>.</p> <p>Visualisation of sampled PBRs and Textures can be seen in: <a href="../records/11391127/files/PBR_examples.jpg?download=1" target="_blank" rel="noopener">PBR_examples.jpg</a> and <a href="../records/11391127/files/Textures_Examples.jpg?download=1" target="_blank" rel="noopener">Textures_Examples.jpg</a></p> <p><a href="https://sites.google.com/view/infinitexture/home" target="_blank" rel="noopener">Link to the main project page</a></p> <p><a href="https://www.arxiv.org/pdf/2403.03309" target="_blank" rel="noopener">Link to paper</a></p> <p> </p> <h2>File structure</h2> <p>Texture images are given in the <strong><a href="../records/11391127/files/Extracted_textures_1.zip?download=1" target="_blank" rel="noopener">Extracted_textures_</a>*.zip</strong> files.</p> <p>Each image in this zip file is a single texture, the textures were extracted and cropped from the <a href="https://storage.googleapis.com/openimages/web/index.html" target="_blank" rel="noopener">open images dataset</a>. </p> <p> </p> <p>PBR Materials are available in <strong><a href="../records/11391127/files/PBR_O0_1.zip?download=1" target="_blank" rel="noopener">PBR_*.zip</a></strong> files these PBRs were generated from the texture images in an unsupervised way (with no human intervention). Each subfolder in this file contains the properties map of the PBRs (roughness, metallic, etc, suitable for blender/unreal engine). Visualization of the rendered material appears in the file Material_View.jpg in each PBR folder.</p> <p>PBR materials and textures which are larger then 512x512 pixels contain '<strong>large' </strong>or<strong> 'larger' </strong>in their file name there about 40k in this repository but 100k more can be found in the <a href="https://sites.google.com/view/infinitexture/home" target="_blank" rel="noopener">main project page</a>.</p> <p>PBR materials and textures who are seamless marked as contain<strong> 'seamless'</strong> in their file name. </p> <p> </p> <p>PBR materials that were generated by mixing other PBR materials are available in files with the names<a href="../records/11391127/files/PBR_mix_O2_2.zip?download=1"> </a><strong><a href="../records/11391127/files/PBR_mix_O2_2.zip?download=1">PBR_mix*.zip</a> </strong></p> <p> </p> <p>Samples for each case can be found in files named: <strong> Sample_*.zip</strong></p> <p> </p> <p>File with the word <strong>Seamless </strong>contain tileable seamless textures this files also contain textures and PBR>512 size that were extracted from the Segment Anything Dataset.</p> <p>Since the textures were extracted fom natural images they were not natively seamless but were turned to seamless using the code at <strong><a href="https://github.com/sagieppel/convert-image-into-seamless-tileable-texture">this url</a></strong></p> <p> </p> <p> </p> <p><strong>Documented code used to extract the textures and generate the PBRs is available at: </strong></p> <p><strong><a href="../records/11391127/files/Texture_And_Material_ExtractionCode_And_Documentation.zip?download=1" target="_blank" rel="noopener">Texture_And_Material_ExtractionCode_And_Documentation.zip</a></strong></p> <h2>Details:</h2> <p>The materials and textures were extracted from real-world images using an unsupervised extraction method (code supplied). As such they are far more diverse and wide in scope compared to existing repositories, at the same time they are much more noisy and contain more outliers compared to existing repositories. This repository is probably more useful for things that demand large-scale and very diverse data, yet can use noisy and lower quality compared to professional repositories with manually made assets like ambientCG. It can be very useful for creating machine learning datasets, or large-scale procedural generation. It is less suitable for areas that demand precise clean and categorized PBR like CGI art and graphic design. For preview It is recommended to look at <a href="../records/11391127/files/PBR_examples.jpg?download=1" target="_blank" rel="noopener">PBR_examples.jpg</a> and <a href="../records/11391127/files/Textures_Examples.jpg?download=1" target="_blank" rel="noopener">Textures_Examples.jpg</a> or download the Sample files and look at the Material_View.jpg files to visualize the quality of the materials.</p> <h3>Scale:</h3> <p>Currently, there are a few hundred of thousands PBR materials and textures but the goal is to make this into over a million in the near future.</p> <h2>Data generation code:</h2> <p>The Python scripts used to extract these assets are supplied at: </p> <p><strong><a href="../records/11391127/files/Texture_And_Material_ExtractionCode_And_Documentation.zip?download=1" target="_blank" rel="noopener">Texture_And_Material_ExtractionCode_And_Documentation.zip</a></strong></p> <p>The code could be run in any folder of random images extract regions with uniform textures and turn these into PBR materials. </p> <h2>Alternative download sources:</h2> <p>Alternative download sources:</p> <p><a href="https://sites.google.com/view/infinitexture/home" target="_blank" rel="noopener">https://sites.google.com/view/infinitexture/home</a></p> <p><a href="https://e.pcloud.link/publink/show?code=kZON5TZtxLfdvKrVCzn12NADBFRNuCKHm70" target="_blank" rel="noopener">https://e.pcloud.link/publink/show?code=kZON5TZtxLfdvKrVCzn12NADBFRNuCKHm70</a></p> <p><a href="https://icedrive.net/s/jfY1xSDNkVwtYDYD4FN5wha2A8Pz" target="_blank" rel="noopener">https://icedrive.net/s/jfY1xSDNkVwtYDYD4FN5wha2A8Pz</a></p> <p> </p> <h2>Paper</h2> <p>This work was done as part of the paper "<a href="https://www.arxiv.org/pdf/2403.03309" target="_blank" rel="noopener">Learning Zero-Shot Material States Segmentation,</a></p> <p><a href="https://www.arxiv.org/pdf/2403.03309" target="_blank" rel="noopener">by Implanting Natural Image Patterns in Synthetic Data</a>".</p> <p>@article{eppel2024learning,</p> <p> title={Learning Zero-Shot Material States Segmentation, by Implanting Natural Image Patterns in Synthetic Data},</p> <p> author={Eppel, Sagi and Li, Jolina and Drehwald, Manuel and Aspuru-Guzik, Alan},</p> <p> journal={arXiv preprint arXiv:2403.03309},</p> <p> year={2024}</p> <p>}</p> <p> </p> <h2>License:</h2> <p>All the code and repositories are available on CC0 (free to use) licenses.</p> <p>Textures were extracted from the open images dataset which is an Apache license.</p>
Machine Eye for Defects: Machine Learning-Based Solution to Identify and Characterize Topological Defects in Textured Images of Nematic Materials
<p><strong>Our paper has been published on Phys. Rev. Res. (doi: 10.1103/PhysRevResearch.6.013259)</strong></p> <p><strong>Our preprint paper is also avilable at arXiv(https://arxiv.org/abs/2310.06406), here is the abstract of our paper:</strong></p> <p>Topological defects play a key role in the structures and dynamics of liquid crystals (LCs) and other ordered systems. There is a recent interest in studying defects in different biological systems with distinct textures. However, a robust method to directly recognize defects and extract their structural features from various traditional and nontraditional nematic systems remains challenging to date. Here we present a machine learning solution, termed Machine Eye for Defects (MED), for automated defect analysis in images with diverse nematic textures. MED seamlessly integrates state-of-the-art object detection networks, Segment Anything Model, and vision transformer algorithms with tailored computer vision techniques. We show that MED can accurately identify the positions, winding numbers, and orientations of ±1/2 defects across distinct cellular contours, sparse vector fields of nematic directors, actin filaments, microtubules, and simulation images of Gay–Berne particles. MED performs faster than conventional defect detection method and can achieve over 90% accuracy on recognizing ±1/2 defects and their orientations from vector fields and experimental tissue images. We further demonstrate that MED can identify defect types that are not included in the training data, such as giant-core defects and defects with higher winding number. Remarkably, MED can provide correct structural information about ±1 defects. As such, MED stands poised to transform studies of diverse ordered systems by providing automated, rapid, accurate, and insightful defect analysis.</p> <p> </p> <p><strong>Repository Organization</strong></p> <p><strong>Trained Models.zip</strong></p> <p>This directory is integral for model deployment and houses all relevant pre-trained models.</p> <ul> <li><strong>plus_vit_vecUV.pt</strong>: Pre-trained model for the Plus Transformer variant.</li> <li><strong>minus_vit_theR.pt</strong>: Pre-trained model for the Minus Transformer variant.</li> <li><strong>nanodet-plus-m_416-halfenhance</strong>: A sub-directory containing all files associated with the trained Nanodet-Plus model.</li> <li><strong>configs</strong>: Configuration files for training procedures.</li> </ul> <p><strong>Training Data.zip</strong></p> <p>This directory contains all datasets used for the training of Nanodet-Plus, Plus Transformer, and Minus Transformer models.</p> <p><strong>Code.zip</strong></p> <p>This directory features the implementation details and example use-cases showcased in Figure 2 and Figure 3c of our associated paper. The directory also includes code corresponding to the specific versions of Nanodet-Plus and SAM models cited in our study.</p> <ul> <li><strong>nanodet</strong>: Code in this folder is adapted from <a href="https://github.com/RangiLyu/nanodet">RangiLyu/nanodet</a> (https://github.com/RangiLyu/nanodet). We have included the exact version used for compatibility.</li> <li><strong>segment_anything</strong>: Code sourced from <a href="https://github.com/facebookresearch/segment-anything">Facebook Research's segment-anything</a> (https://github.com/facebookresearch/segment-anything). The specific version used is included for compatibility.</li> <li><strong>Fig2</strong>: Code for predicting topological defects in tissue cell images, citing the following reference: T. B. Saw et al., Nature 544, 212 (2017).</li> <li><strong>Fig3c</strong>: Code for predicting topological defects in microtubules images, citing the following reference: M. Golden et al., Sci. Adv. 9, eabq6120 (2023).</li> </ul> <p><strong>Initialization Steps</strong></p> <p>Before executing any code, please ensure the following:</p> <ul> <li>All files in the <strong>Trained Models</strong> directory must be available.</li> <li>Download the checkpoint <strong>sam_vit_l_0b3195.pth</strong> from <a href="https://github.com/facebookresearch/segment-anything">Facebook Research's segment-anything</a>. (https://github.com/facebookresearch/segment-anything)</li> </ul> <p><strong>Acknowledgments</strong></p> <ul> <li><a href="https://github.com/RangiLyu/nanodet">RangiLyu/nanodet</a> (https://github.com/RangiLyu/nanodet)</li> <li><a href="https://github.com/facebookresearch/segment-anything">Facebook Research's segment-anything</a> (https://github.com/facebookresearch/segment-anything)</li> </ul> <p>For further inquiries or issue reporting, you may contact us via email.</p> <p><strong>Contact Information</strong>: <a href="mailto:hrenae@connect.ust.hk">hrenae@connect.ust.hk</a></p>
Data and supplementary material for the paper "A simple image analysis technique for measuring bed surface texture in flume experiments"
<p>This repository contains the Matlab codes developed for the image processing illustrated in the paper "A simple image analysis technique for measuring bed surface texture in flume experiments" submitted to the Journal of Hydrology. Results of the image processing are reported in some excel spreadsheets. We also provide the pdf file of the paper "Morphology, bedload and sorting process variability in response to lateral confinement: results from physical models of gravel-bed rivers" illustrating the laboratory experiments for which we developed the image analysis technique presented in "A simple image analysis technique for measuring bed surface texture in flume experiments". The paper titled "Morphology, bedload and sorting process variability in response to lateral confinement: results from physical models of gravel-bed rivers" has not been published yet, but it has been accepted by the Journal of Geophysical Research - Earth Surface. Thus, we provide the editor acceptance letter as well.</p>
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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.