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422 results for “Texturing”
Ballynahunt ogham stone (textured)
Created using photogrammetry (54 images) and processed with Agisoft Photoscan software (untextured version available here: https://sketchfab.com/models/c98aba034b9a40fe9e855be1adc23046. Tradition is that this stone originally came from near a well 'up the mountain east of Ballynahunt', near Annascaul, Co. Kerry. Macalister (1945, no. 171) read the inscription as DUGENNGG[I] MAQI RODDOS, which is up-side-down in relation to the cross. The stone is currently attached to the gable end of a farmhouse. Part of the inscription is beneath ground level and many of the vowel notches are unclear. Source: Objaverse 1.0 / Sketchfab
Lesingey Round: textured digital surface model
Lesingey Round is a hillfort near Penzance in Cornwall, and likely dates to the Iron Age. The monument consists of a roughly circular enclosure surrounded by ramparts with a ditch on the outside. From the partially buried ditch, the ramparts stand over 4.5m high in places. In other areas the ramparts have been damaged. The site sits atop a prominent hill with panoramic views, and is covered by a 'crown' of trees that cover the site. In this model the trees have been digitally removed to allow for a different understanding of the site. This 3D survey was undertaken by the [Curatorial Research Centre](https://curatorialresearch.com) on behalf of Penwith Landscape Partnership (PLP). The site is owned and maintained by the [Cornish Ancient Sites Protection Network](https://cornishancientsites.com/) (CASPN). Source: Objaverse 1.0 / Sketchfab
Ballinrannig ogham stone VII (textured)
'In 1782 a storm blew away an accumulation of sand, and revealed a series of seven Ogham-inscribed stones, in an ancient burial-ground, called Kilvickillane, on the shore of Smerwick Bay' on the Dingle Peninsula. This is the only ogham stone now remaining on site. Macalister (1945, 149-50, no. 154) read it as CUNAMAQQI CORBBIMAQQ[I MUCCOI DOVVINIA]S. Letters in square brackets are a possible reconstruction, no evidense for these survives on the stone. 3d model created using photogrammetry from 74 images and processed using Agisoft PhotoScan. Source: Objaverse 1.0 / Sketchfab
Heritage library in Gliwice - 3D textured mesh
3d textured mesh of heritage library destined for demolition. Model created by POI Format from photos taken with UAV, processed with photogrammetric methods. Source: Objaverse 1.0 / Sketchfab
Axe Texture Paiting
Exercise 1 of Fundamentals of Texturing from CGCookie's Introduction to Blender Flow... Source: Objaverse 1.0 / Sketchfab
White wall texture
Tileable pbr white wall texture. 4096x4096px. Files: - Basecolor - Normal map - Height map - Roughness map - Ambient Occlusion map Source: Objaverse 1.0 / Sketchfab
Textured Ancient Cappella
Ruins of a small chapel in Italy. 3D Mesh created and textured in 3DReshaper. Try to create it yourself by downloading the exercise: http://www.3dreshaper.com/en/software-en/support-software-en/practical-exercises Source: Objaverse 1.0 / Sketchfab
Rathduff II ogham stone (textured)
One of two ogham stones discovered in the graveyard and site of the medieval parish church of Ballinvoher, in the townland of Rathduff, near Annascaul, Co. Kerry. 3d model created using photogrammetry (24 images) and processed with Agisoft PhotoScan Source: Objaverse 1.0 / Sketchfab
Sword FBX with Texture
Sword Texture from Year 2 design. #Model #Sword #Black #Gray #Samurai Source: Objaverse 1.0 / Sketchfab
Boglosa 298 O Rickeby Textur
Boglösa 298, Övre Rickeby, Uppland. Hällristning. Opus Heritas Fotogrammetri 3D-SfM av Catarina Bertilsson. Stockholms universitet, "Digitala bilder för forskning och publik". Source: Objaverse 1.0 / Sketchfab
3D Modeling and texturing practice 03
My third 3D Modeling and texturing practice. My take on Imanol Delgado Salazar's Tanvaasa Axe. Source: Objaverse 1.0 / Sketchfab
Moroccan Bowl - Texture & lighting test
Moroccan Bowl - Texture & lighting test Attempt to correctly reproduce glaze and texture Source: Objaverse 1.0 / Sketchfab
Boglosa 125 Rickeby Droner Texture
Boglösa 125, Rickeby, Uppland. Drönare. Hällristning. Opus Heritas, Fotogrammetri 3D-SfM av Catarina Bertilsson. Stockholms universitet, "Digitala bilder för forskning och publik". Source: Objaverse 1.0 / Sketchfab
Bred 104 Boda Hela Textur
Bred 104, Boda, Uppland. Hällristning hela ytan med textur. Opus Heritas, Fotogrammetri 3D-SfM av Catarina Bertilsson. Stockholms universitet, "Digitala bilder för forskning och publik". Source: Objaverse 1.0 / Sketchfab
Processing of haptic texture information over sequential exploration movements
<p>Where textures are defined by repetitive small spatial structures, exploration covering a greater extent will lead to signal repetition. We investigated how sensory estimates derived from these signals are integrated. In Experiment 1 participants stroked with the index finger one to eight times across two virtual gratings. Half of the participants discriminated according to ridge amplitude, the other half according to ridge spatial period. In both tasks just noticeable differences (JNDs) decreased with an increasing number of strokes. Those gains from additional exploration were over 3 times smaller than predicted for optimal observers who have access to equally reliable, and therefore equally weighted estimates for the entire exploration. We assume that the sequential nature of the exploration leads to memory decay of sensory estimates. Thus, participants compare an overall estimate of the first stimulus, which is affected by memory decay, to stroke-specific estimates during the exploration of the second stimulus. This was tested in Experiments 2 & 3. The spatial period of one stroke across either the first or second of two sequentially presented gratings was slightly discrepant from periods in all other strokes. This allowed calculating weights of stroke-specific estimates in the overall percept. As predicted, weights were approximately equal for all strokes in the first stimulus, while weights decreased during the exploration of the second stimulus. A quantitative Kalman filter model of our assumptions was consistent with the data. Hence, our results support an optimal integration model for sequential information given that memory decay affects comparison processes.</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>
Soil C saturation in tropical agricultural systems: soil fertility, climate, texture and N controls on SOM fractions
<p><span>Nature-based solutions for C sequestration in tropical croplands are paramount strategies in a changing climate. Soybean-maize-forage intercropped systems associated with soil acidity alleviation and nitrogen (N) fertilization effectively accumulate carbon (C) in weathered soils. However, the soil saturation status and the controls of C stabilization up to 40 cm soil depth into particulate (POM) and mineral-associated organic matter (MAOM) fractions are poorly understood in tropical croplands. This study tested the hypothesis of C saturation in the MAOM fraction by comparing field and published data focused on Brazilian tropical soils. We assessed the N fertilization effect on C stabilization in POM and MAOM pools, and climate, soil texture, and chemical attributes controls on POM and MAOM formation in tropical croplands using a machine-learning Random Forest (RF) modeling approach. Our findings do not support C saturation in the MAOM regardless of contrasting soil contents of silt plus clay and depths. C in the MAOM and POM fractions were not affected by N fertilizer. However, legume inclusion in the system resulted in higher total soil C and lower soil C:N ratio compared with N fertilization, which indicates that C dynamics differ whether synthetic or organic-N forms are applied to tropical croplands. Our RF model showed robust predictive performance for the MAOM but poorer for the POM fraction. Total organic carbon (TOC), total N, silt plus clay, phosphorus (P) and soil depth, zinc (Zn), cation exchange capacity, and TOC covariates were the most important variables for predicting MAOM and POM fractions, respectively. Our findings show, besides the well-known effect of calcium, that other nutrients such as P, Zn, manganese and copper play a key role in either MAOM or POM formation. Our work provides key insights on C saturation status in tropical croplands and land-management C sequestering potential, and N, climate, soil chemical attributes and texture controls on C distribution into MAOM and POM fractions up to 40 cm soil depth in tropical conservation agricultural systems. We advocate for upscaling C saturation concept nationally and further research investigating the role of climate and micronutrients contribution on MAOM and POM formation in broad scenarios and N fertilizer responsiveness for C stabilization in tropical croplands.</span></p>
CPFEM Goss Texture
<pre>Stress-Strain Analysis of a Polycrystal with Goss Texture</pre>
Particle-hole asymmetric ferromagnetism and spin textures in the triangular Hubbard-Hofstadter model
<p>Aggregated numerical data and analysis routines required to reproduce the figures in "Particle-hole asymmetric ferromagnetism and spin textures in the triangular Hubbard-Hofstadter model"</p>
Figure 10 in Bone surface texture as an ontogenetic indicator in long bones of the Canada goose Branta canadensis (Anseriformes: Anatidae)
Figure 10. The relationship between texture type and date of death.
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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.