Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
32
datasets available to search
ShareScore release 0.9.0
Dataset results
32 results for “3d point cloud”
LigPCDS: Labeled Dataset of X-ray Protein Ligand Images in 3D Point Cloud and Validated Deep Learning Models
<p>The difference electron density from X-ray protein crystallography was used to create the first dataset of labeled ligand images in 3D point clouds, named <strong>LigPCDS</strong>. The dataset contain 244,226 entries of free organic ligands containing 3D representations labeled with two major labeling approaches: SP-based and AtomSymbol-based.</p> <p> </p> <p>The data from free organic molecules (non-covalent ligands) was retrieved from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) in december 2019 with resolutions ranging from 1.5 to 2.2 Å. The ligand images (blobs) were interpolated from their calculated difference electron density map in a 3D grid-like bounding box, around their atomic positions, and stored in point clouds. These ligand grid representations were further processed to retrive the final ligands representation in 3D point clouds using a mask of the shape of the ligand. A grid spacing of 0.5 Å gave the best results. The density value of the grid points was used as feature. The labeling approach used the structure of the ligands to propose vocabularies of chemical classes based on the chemical atoms themselves and their cyclic substructures. These structure annotations were applied pointwise to the ligand 3D representations using an atomic sphere model. Four proposed vocabularies were validated by successfully training good performance deep learning models for the semantic segmentation of a stratified dataset from LigPCDS, using 78902 entries.</p> <p>The four validated deep learning models are: (i) the LigandRegion, composed by generic atoms of any type; (ii) the AtomCycle, composed by generic atoms outside cycles and generic cycles; (iii) the AtomC347CA56, composed by generic atoms outside cycles, not aromatic cycles of size 3 to 7 and aromatic cycles of size 5 and 6; and (iv) the AtomSymbolGroups, composed by the atoms symbols with groupings. The mean accuracy of these models in their cross-validation was between 49.7% <span lang="EN-GB">[-19.4,20.</span><span lang="EN-GB">2]</span> and 77.4% <span lang="EN-GB">[-11.7,12.1]</span> in terms of Intersection over Union (mIoU) metric and between 62.4% <span lang="EN-GB">[-18.8,19.</span><span lang="EN-GB">7]</span> and 87.0% <span lang="EN-GB">[-8.4,8.8]</span> in F1-score (mF1), confidence interval between squared brackets. The models i, ii and iii and the used labeled representations in 3D point cloud are contained in the SP-based record; and model iv and its used labeled representations are contained in the AtomSymbol-based record.</p> <p>The dataset and validated models may be used to tackle problems regarding known and unknown ligand building to drug discovery and fragment screening pipelines. </p> <p>The code used to create and validated the LigPCDS is available at the following repository: https://github.com/danielatrivella/np3_ligand</p> <p>This repository also contains the NP³ Blob Label application for ligand building using the validated deep learning models from LigPCDS.</p>
RGB orthophoto mosaic, DSM, 3d point cloud and LIDAR LAZ of the flash flood damages in Karavelovo and Bogdan vilages, Bulgaria- September 2, 2022
<p>The present dataset contains geospatial resources aimed at investigating and assessing the consequences of a flash flood of debris flow character, relatively significant in extent and magnitude of damage, in the area of two villages in the Municipality of Karlovo, located in central Bulgaria, which happened on September 2, 2022. For this purpose, an integrated approach based on the combination of digital photogrammetry with high spatial resolution and spatial accuracy, based on a fixed wing unmanned aerial system, and laser altimetry (LIDAR), based on a multirotor unmanned platform, was used. The data collection was carried out 2 days after the occurrence of the disaster, resulting in the generation of valuable information resources that allow not only to spatially and quantitatively determine the damage of the disaster, but also to reveal the mechanism of occurrence of the phenomenon: 1) orthophoto mosaic, Digital surface model-DSM and 3D point cloud (from photogrammetry) 2) Classified 3D point cloud- from LIDAR survey.</p>
Sila National Park - 3D Point cloud data
<p>This dataset contains 3 types of data.</p> <ul> <li>GPS data (the ones starting with <em>"GPS"</em>) of sampling plot centers collected with a Trimble GPS and post processed to ensure positioning errors lower than 2 meters.</li> <li>TLS data, (the ones starting with <em>"ID_"</em>): such data were collected in the end of August 2019 with a mobile terrestrial laser scanner (mobile ZEB TLS) in a squared area of approximatively 30x30m. Data have been normalized using TreeLS package in R.</li> <li>ALS data collected in the end of July 2019. For the entire study area, we upload 2 different ALS data: "<em>merged.las</em>" is the original point cloud; "<em>myLas_norm_lt22.las</em>" is the normalised point cloud, cut at 22 meters from the ground in order to perform specific analysis (i.e. paper under submission).</li> </ul> <p>Data collection was founded by the <em>AGRIDIGIT Selvicoltura</em> project.</p>
Precision viticulture dataset for detailed vineyard mapping composed of geotagged smartphone ground images, phytosanitary status, UAV orthomosaics, 3D point clouds, and RTK GNSS data - Northern Spain, July 2022
<p>This dataset offers a rich multimodal collection of data from vineyards, designed to enhance agricultural research with a focus on vineyard management and disease monitoring. It includes geotagged smartphone ground images in ".7z" format for detailed plant-level analysis, a ".csv" file detailing plants' phytosanitary status for health assessment, UAV-derived 3D Point Clouds and orthomosaics in ".las" and ".tiff" formats for aerial landscape views, and RTK GNSS data in ".shp" format for precise plant geolocations.</p> <p>This dataset can be combined with other datasets to enable a comprehensive view of the vineyards and improve its value:</p> <div> <ul> <li>Ariza-Sentís, Mar, Sergio Vélez, and João Valente. ‘Dataset on UAV RGB Videos Acquired over a Vineyard Including Bunch Labels for Object Detection and Tracking’. <em>Data in Brief</em> 46 (February 2023): 108848. <a href="https://doi.org/10.1016/j.dib.2022.108848">https://doi.org/10.1016/j.dib.2022.108848</a>.</li> <li>Vélez, Sergio, Mar Ariza-Sentís, and João Valente. ‘VineLiDAR: High-Resolution UAV-LiDAR Vineyard Dataset Acquired over Two Years in Northern Spain.’ <em>Data in Brief</em>, October 2023, 109686. <a href="https://doi.org/10.1016/j.dib.2023.109686">https://doi.org/10.1016/j.dib.2023.109686</a>.</li> <li> <div> <div>Vélez, Sergio, Mar Ariza-Sentís, and João Valente. ‘Dataset on Unmanned Aerial Vehicle Multispectral Images Acquired over a Vineyard Affected by Botrytis Cinerea in Northern Spain’. <em>Data in Brief</em> 46 (February 2023): 108876. <a href="https://doi.org/10.1016/j.dib.2022.108876">https://doi.org/10.1016/j.dib.2022.108876</a>.</div> <div> </div> </div> </li> </ul> </div>
Large-scale 3D building and tree datasets constructed from airborne LiDAR point clouds in Glasgow, UK
<p>This is the updated version of building 3D model data. The revision includes appending attributes to the lod1 and lod2 shapefile and creating cityjson file for each 3D building model. All 3D building models are available in mesh (.obj), multipath shapefile, and cityjson (.json) now.</p> <p><strong>IMPORTANT NOTE: We suggest using the building footprint, lod1, and lod2 data of this version (Version v4).</strong></p> <p>Urban Big Data Centre of the University of Glasgow generates 3D city models via the airborne LiDAR point clouds acquired between 2020-2021 on behalf of Glasgow City Council. It is a large-scale 3D city model containing 3D information on terrain, trees, and buildings in Glasgow City. This dataset comprises terrain, tree canopy, and building products derived from high-density airborne LiDAR point clouds. </p> <p>The terrain products include Digital Terrain Model (DTM), Digital Surface Model (DSM), and normalized Digital Surface Model (nDSM) in 0.5 m spatial resolution. The DTM and DSM rasters were provided by the vendor and nDSM rasters were obtained by subtracting DTM from DSM. Terrain products are provided in 5 km by 5 km GeoTIF format raster.</p> <p>The tree canopy products are composed of canopy height models (CHM) and tree top locations. Classified tree point clouds were applied with pit-free algorithm to generate CHM in 0.5 m grid raster in GeoTIF format [1]-[2]. Treetop locations were identified by using Local Maximum Filter based on CHM and are recorded as points in Shapefile format. The tree canopy products are provided in 5 km by 5 km tiles.</p> <p>Building 3D model products include footprint polygons with building height attributes and 3D mesh of building models in LoD1 and LoD2 levels. A series of processes such as converting building point clouds to building height models (BHM), converting BHM to polygons, and polygon regularization were conducted to obtain the building footprint polygons. Building height attributes were calculated from BHM for each footprint. The building footprint data are provided in Shapefile format. LoD1 models were generated based on the footprint and average height of the building. LoD2 models were constructed based on footprint and building point cloud with City3D tool[3]. LoD1 and LoD2 models are provided in OBJ and shapefile format. Building 3D model products are provided in 5 km by 5 km tiles. The RMSE of Euclidean distances between each point in the point cloud to the reconstructed model was calculated to evaluate the LoD2 model construction. A table of RMSE and a note for a few problematic models are provided.</p>
Multimodal Dataset of 3D point clouds and CT-volumes
<p>The multimodal dataset for evaluating algorithms for aligning CT volumes and point clouds which is presented in 'Multimodal registration across 3D point clouds and CT-volumes'. (Saiti, E., and T. Theoharis. "Multimodal registration across 3D point clouds and CT-volumes." <em>Computers & Graphics</em> 106 (2022): 259-266.) The multimodal dataset consistsof real micro-CT scans and their synthetically generated 3D models (point clouds) .</p>
3D Point Cloud of a railway slope - MOMIT (Multi-scale observation and monitoring of railway infrastructure threats) EU project - H2020-EU.3.4.8.3. - Grant agreement ID: 777630
<p>3D point cloud of a railway trench in Lavancia-Épercy (France). The 3D point cloud has been generated from pictures obtained by means of a UAV (DJI Matrice 600 Pro) and processed using Agisoft Metashape. The 3D point cloud is composed of 110,356,682<strong> </strong> million points containing XYZ and RGB information.</p> <p>The original file is in .bin format and is compressed in zip format.</p> <p> </p>
dataset for "basic setting", "+ binary semantic loss", "+ class weights", "+ height weights", "+ region weights", "+ elastic distortion and subsampling", "+ TreeMix" in paper Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning
<p>dataset for "basic setting", "+ binary semantic loss", "+ class weights", "+ height weights", "+ region weights", "+ elastic distortion and subsampling", "+ TreeMix" in paper Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning</p>
Cappadocia Mobile LiDAR 3D Point Cloud Dataset
<p>The dataset includes 6 3D point cloud files collected with Velodyne VLP-16 mobile LiDAR (*.las) belonging to 4 cultural and natural heritage structures located in Cappadocia, Türkiye. The structures are:</p> <p>1- St. Theodore Church (Interior & Exterior): The Church of St. Theodore is located in Yeşilöz Village in Ürgüp district of Nevşehir. Formerly known as Tagar, now known as Yesiloz Village is approximately 16 km from the center of Urgup district and is a settlement area built on the slope of the valley. The church was carved into a large rock mass on the hill northwest of the village. As a result of excavations near the village, a monastery with a courtyard on three sides was discovered. It is thought that the church belonged to this monastery. The church is called both St. Theodore and Tagar Church. Although it is not known where the name Theodore comes from, it is estimated that this name may have been given because the church was built in the name of St. Theodore. </p> <p>2- Mustafa Efendi Mosque (Interior & Exterior): The masonry Mustafa Efendi Mosque in Bahçeli Village of Ürgüp District of Nevşehir Province is the oldest of the 3 mosques built in the village. It is estimated that it was built about 50 years before the Osman Efendi Mosque, which was presented as a proposed building within the scope of the project, with a construction date of 1746. Although it is known to have a small inscription with the date of construction, this inscription was not found during the survey. According to this information, Mustafa Efendi Mosque is estimated to be a 17th-18th century work.</p> <p>3- Fairy Chimney: The distance between Bahçeli Village where the fairy chimney is located and Urgup district is 15 kilometers and the formations between these two areas are generally natural formations without caps and in the late fairy chimney period. It shows that the fairy chimney is a natural formation without a cap and in the late fairy chimney period. The fairy chimney is in the 1st degree natural protected area.</p> <p>4- Masonry House: Bahçeli Village, where the building examined within the scope of the project is located, is 15 km away from Ürgüp district and is a mixed settlement type. There are approximately 200 cove-carved and masonry historical buildings in the village. A large part of the village, including the structures examined in the village, is a 3rd degree natural protected area. The masonry-rock-carved civil architecture dwelling in Bahçeli Village, Ürgüp District, Nevşehir has not been in use since the 1980s and some of the spaces have been completely lost.</p> <p>The creation of this dataset was funded by the Scientific and Technological Research Council of Türkiye (TUBITAK) 1001 program under Project no. 122Y017.</p> <div> <div> <div> </div> <div> </div> <div> </div> </div> </div>
Vegetation Stratum Occupancy Prediction from Airborne LiDAR 3D Point Clouds
<p>Our proposed dataset comprises 199 cylindrical plots of 10 m radius corresponding to typical pasture land parcels in South-Eastern France. Each plot contains between 3000 and 17000 3D points, and each point is attributed with a total of 10 features: (i) absolute 3D coordinates, (ii) RGB and Near-InfraRed reflectance obtained with aerial cameras, (iii) uncalibrated laser intensity, return number and number of returns provided by the aerial LiDAR.</p> <p>This dataset can be used as training data for our model deep learning model "Vegetation Stratum Occupancy Prediction from Airborne LiDAR 3D Point Clouds" available on <a href="https://github.com/ekalinicheva/plot_vegetation_coverage">https://github.com/ekalinicheva/plot_vegetation_coverage</a></p> <p> </p>
UAV RGB imagery dataset captured at nadir and oblique angles over pistachio trees in Spain, including images, GCPs, 3D point cloud and orthomosaic.
<p>The dataset comprises 248 images taken in two flights on 29 July 2021 over a pistachio orchard in Spain. In addition, GCPs (ground control points) were collected to improve the photogrammetric process accuracy. The photos were taken using a UAV DJI Phantom Advance quadcopter equipped with a DJI FC6310 RGB 20-megapixel camera. The first flight mission was planned to take nadir images (-90º gimbal pitch degree), whereas the second flight was scheduled to take oblique images (-60º gimbal pitch degree), both at 55 metres above the ground. In addition, the images were used to generate a 3D point cloud, DEM and orthomosaic, which were included in the dataset.This dataset is useful for precision agriculture researchers interested in photogrammetric reconstruction.</p>
DEM, rock point clouds, and 3D morphological features of Martian rocks in the landing region of Zhurong rover
<p>Rocks on the Martian surface and their three-dimensional (3D) morphology record the geological evolution of Mars and its interaction history with outer space. China's Zhurong Mars rover has traveled on Mars for nearly 2 kilometers and obtained a wealth of data, enabling the fine-scale measurement and morphological analysis of 3D Martian rocks. With 178 high-resolution Zhurong NaTeCams stereo images acquired in rings covering large circular areas, we extracted 6,185 3D rocks from centimeter-scale to meter-scale distributed along the traverse of the Zhurong rover using an automatic approach. This dataset includes the digital elevation model (DEM), extracted rocks in point clouds, and the 3D morphological features of rocks derived from the Zhurong NaTeCams images, obtained in six areas along the traverse of the Zhurong rover.</p> <p>To use this dataset, please cite "Li, Y., Xiao, Z., Ma, C., Zeng, L., Zhang, W., Peng, M., & Li, A. (2023). Extraction and Analysis of Three‐dimensional Morphological Features of Centimeter‐scale Rocks in Zhurong Landing Region. <em>Journal of Geophysical Research: Planets</em>, e2022JE007656."</p>
3D Point Cloud Data for LiDAR-based Mobile Robot
<p>LiDAR point cloud data serves as an machine vision alternative other than image. Its advantages when compared to image and video includes depth estimation and distance measurement. Low-density LiDAR point cloud data can be used to achieve navigation, obstacle detection and obstacle avoidance for mobile robots. autonomous vehicle and drones. In this metadata, we scanned over 1400 objects and classified it into 6 groups of object namely, human, cars, motorcyclist, signboard, road divider and others.</p>
Shadowcasting from 3D point clouds
<p>This video shows a time-lapse of shadows modelled by using a 3D point cloud of an urban garden, Villa Revedin Bolasco. </p>
Innovative 3D Cone Point Cloud Fitting via Landweber Iteration
<p>Cone surface fitting is essential in various fields, including computer graphics, computer vision, and robotics. However, factors such as noise, initial parameter selection, and the varying distribution of point clouds can significantly impact fitting accuracy and stability. To address these challenges, we propose a novel optimization method based on Landweber iteration. This method solves an objective function that includes the cone vertex. Initially, Landweber iteration is used to calculate high-precision initial values for the conical surface by leveraging all point cloud data. Subsequently, the RANSAC algorithm filters the point cloud data based on these initial values, eliminating points that do not meet the distance threshold. Finally, an error equation is formulated, and the cone surface parameters are further refined using an optimization algorithm based on Landweber iteration. Simulation experiments validate the feasibility and robustness of our approach, demonstrating superior accuracy and stability in 3D cone surface fitting.</p>
Points cloud of 3D CAD models of artificial reefs designed by E.Riera from "Unleashing the Potential of Artificial Reefs Design" (txt files)
<p>Here you will find points cloud of 3D CAD models in txt format.</p> <p>These are 3D CAD models of artificial reefs designed by E.Riera from the paper "Unleashing the Potential of Artificial Reefs Design" (<a href="https://doi.org/10.32942/X2G300">https://doi.org/10.32942/X2G300</a>).</p> <p>These txt files (with others not available on open access) have been used to run a script on R "Computation of fractal dimension" to compute the fractal dimension from the point clouds using the Minkowski-Bouligand method (or "box-counting") using the R statistical framework (version 4.0.3) and “est.boxcount” function of the package “Rdimtools” (You and Shung, 2022). </p>
Lung250M-4B: A Combined 3D Dataset for CT- and Point Cloud-Based Intra-Patient Lung Registration
<p>Point Cloud Data from the Lung250M-4B dataset.</p><p>Visit https://github.com/multimodallearning/Lung250M-4B for image data and associated code.</p>
Point-Cloud (3D Scan)
Point-Cloud 3D Scan of a historical site in northern Switzerland Source: Objaverse 1.0 / Sketchfab
3D Point Clouds of Trees and Apple Fruit Annotated with Thermal Data
<p>The data set captures four measurements during fruit growth: 06/28/2022 (15:00), 07/12/2022 (15:00), 09/01/2022 (15:00), 09/06/2022 (13:00)</p> <p>Additionally, diurnal courses are provided for three days: </p> <table> <tbody> <tr> <td> <p>Date</p> </td> <td> <p>Time</p> </td> </tr> <tr> <td> <p>09/21</p> </td> <td> <p>07:00, 08:00, 10:00, 12:00, 13:00, 18:00</p> </td> </tr> <tr> <td> <p>09/22</p> </td> <td> <p>07:00, 08:00, 10:00, 12:00, 13:00, 18:00</p> </td> </tr> <tr> <td> <p>10/05</p> </td> <td> <p>06:30, 07:00, 09:00, 10:00, 11:00, 16:00</p> </td> </tr> </tbody> </table> <p> </p> <p>The data set was measured in Blocks (A-D) of trees (T) on apple (A) fruit and is stored as compressed zip files, capturing raw, preprocessed, and manually recorded reference (ground truth) data.</p> <p>1. zip files entitled Raw_YYYY_MM_DD_Block[A-C]-[R, L] for seasonal data and Raw_YYYY_MM_DD_BlockD-[R, L]_Hour__:__ for diel data</p> <p>- raw data of LiDAR 3D point clouds - txt files</p> <p>- raw image data by thermal camera - txt files</p> <p>2. zip files entitled YYYY_MM_DD or DailyAcquisitions:</p> <p>- preprocessed (merged) sensor data of temperature-annotated 3D point clouds of canopies - csv files</p> <p>- preprocessed data, capturing manually segmented point clouds of temperature-annotated fruit - txt files</p> <p>3. Microsoft Excel files entitled References and Weather data:</p> <p>- raw data, representing reference data of fruit - xlsx file</p> <p>- raw data of weather conditions - xlsx file</p>
3D spinal column point clouds of mice with RyR1 mutations and their wild-type littermates
<p>Spinal column point clouds of mice with RyR1 mutations and their wild-type littermates.</p> <p>Part of the paper Ruiz et al., "Reduction of RyR1 expression in muscle spindles causes alterations of the proprioceptive properties and scoliosis in mice carrying recessive Ryr1 mutations"</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.