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Dataset results
222 results for “Point cloud”
South Entrance, British Museum [point cloud]
Quick scan of the Museum's iconic South entrance. 25 photos, Canon g7x, Agisoft PhotoScan Source: Objaverse 1.0 / Sketchfab
L'Arc by Urs Fischer (Point cloud)
This is a point cloud of the monumental statue l'Arc by Urs Fischer. This 11 meters high arch in sparkling aluminum, with an undulatory shape, now stands in front of Station F the world's biggest startup campus. This point cloud has been created with and [Ouster](https://ouster.com/) OS1 3D LiDAR sensor with [Exwayz](https://www.exwayz.fr/) SLAM software. Source: Objaverse 1.0 / Sketchfab
Point clouds (LAZ): Towards a spatial data repository for archaeological research in the Romanian Mostiștea Basin and Danube Valley
<p>Spatial data are crucial in archaeological research, where orthophotos, digital elevation models, and 3D models are widely used for mapping, documenting, and monitoring archaeological sites. The introduction of affordable and compact unmanned aerial vehicles (UAVs) has significantly advanced the use of UAV-based photogrammetry in the past 20 years. Recently, compact airborne systems have also enabled the capture of thermal, multispectral, and aerial laser scanning data. This study presents the data acquired with different platforms and sensors at Chalcolithic archaeological sites in Romania's Mostiștea Basin and Danube Valley. Since laser scanning and photogrammetry generate large data volumes, data storage and dissemination must also be carefully considered. Based on a thorough study of system performance, data acquisition and processing methods, and data outputs, a workflow for the systematic mapping and documentation of sites has been proposed. Given the experience obtained in the last 5 summer campaigns (2018-2023), 19 sites have been accurately mapped, of which 5 sites are mapped using airborne laser scanning. 18 sites are documented using multispectral photogrammetry, and for 17 sites, interactive image-based 3D models are acquired using true-color photogrammetry. All data are stored on a publicly accessible website for visualization, as well as on an open-data platform for data exchange. For the multispectral data, a raster tile service has been implemented, allowing the use of the data in a GIS environment.</p>
Plot-level semantically labelled terrestrial laser scanning point clouds
<p><strong>Abstract</strong></p> <p>Point clouds from Terrestrial Laser Scanning (TLS) are an increasingly popular source of data for studying plant structure and function. However, unlocking their full potential currently requires extensive manual processing to extract ecologically important information. One key task is the accurate semantic segmentation of different plant material within point clouds, particularly wood and leaves, which is required to understand plant productivity, architecture, competition, space optimisation and physiology, and is a key step in common approaches to individual tree extraction. Existing automated semantic segmentation methods are primarily developed for single ecosystem types, and whilst they show good accuracy for biomass assessment from the trunk and large branches, often perform less well within the crown. In this study, we demonstrate a new framework that uses a deep learning architecture developed from PointNet++ and pointNEXT for processing 3D point clouds to provide a reliable semantic segmentation of wood and leaf in TLS point clouds from the tree base to branch tips, applied to diverse natural European forests. Our model combines meticulously labelled data with voxel-based sampling and a novel gated reflectance integration module embedded throughout the feature extraction layers. We evaluate its performance across an extensive dataset, encompassing diverse ecosystem types and sensor characteristics. Our results show consistent outperformance against the most widely used PointNet++-based approach for leaf/wood segmentation on a high-density TLS dataset collected across diverse mixed forest plots across all major biomes in Europe. We tested our model against others’ open data from China, Eastern Cameroon, Germany and Finland, collected using both time-of-flight and phase-shift sensors, finding consistently strong performance, showcasing the transferability of our model to a wide range of ecosystems and sensors. Our newly developed evaluation metric for assessing performance in the outer parts of the canopy, such as in twigs and small branches, found our model to clearly outperform the most widely used approach.</p> <p><strong>Methods</strong></p> <p>Within each country, we scanned a subset of the 30 m x 30 m FUNDIV plots using a Riegl VZ400i TLS instrument (RIEGL Gmbh, Horn, Austria), scanning at 600MHz and with an angular resolution of 0.04 mrad. All plots were scanned following a 10m grid system with a minimum of 16 upright and 16 tilt scans (following Wilkes et al. 2017), with additional scans to minimise occlusion in dense areas, and on the plot perimeter. To ensure high-quality data with minimal noise, scanning was paused when wind conditions rose above 5 m/s (measured with an anemometer on the ground) or when gusts were visually evident. In order to create our labelled dataset, we used a semi-automated approach informed by existing approaches followed by significant manual cleaning. Vicari et al. (2019) found anisotropy, verticality and linearity to be informative features for leaf-wood separation, so we created these geometric features at spatial scales of approx. 5 cm - 0.5 m (using CloudCompare, 2023). Alongside these, we used reflectance and xyz information for each point and labelled leaf-wood by thresholding these features. We followed this with intensive manual checking and cleaning to ensure high label quality, especially in the smaller branches and twigs. Our dense scanning and labelling strategy means that this dataset is an ideal candidate for training and testing the capabilities of processing algorithms. Our dataset comprises nine 10 m x 10 m blocks of x, y, z coordinates with corresponding reflectance values and labels, all at 1 cm resolution achieved through voxel downsampling. This data was used for training and validation. For comprehensive evaluation, we incorporated additional openly available datasets, which we cropped to reduce size and cleaned to rectify erroneous labels. The original, unmodified versions of these datasets can be accessed as follows:</p> <div> <p>*Mspace Lab (2024) ‘ForestSemantic: A Dataset for Semantic Learning of Forest from Close-Range Sensing’, Geo-spatial Information Science. Zenodo. <a href="https://doi.org/10.5281/zenodo.13285640">https://doi.org/10.5281/zenodo.13285640</a>. Distributed under a Creative Commons Attribution Non Commercial No Derivatives 4.0 International licence.</p> <p>Wang, Di; Takoudjou, Stéphane Momo; Casella, Eric (2021). LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR [Dataset]. Dryad. <a href="https://doi.org/10.5061/dryad.np5hqbzp6">https://doi.org/10.5061/dryad.np5hqbzp6</a>. Distributed under a Creative Commons 0 1.0 Universal licence.</p> <p>Wan, Peng; Zhang, Wuming; Jin, Shuangna (2021). Plot-level wood-leaf separation for terrestrial laser scanning point clouds [Dataset]. Dryad. <a href="https://doi.org/10.5061/dryad.rfj6q5799">https://doi.org/10.5061/dryad.rfj6q5799</a>. Distributed under a Creative Commons CC0 1.0 Universal licence.</p> <p>Weiser, Hannah; Ulrich, Veit; Winiwarter, Lukas; Esmorís, Alberto M.; Höfle, Bernhard, 2024, "Manually labeled terrestrial laser scanning point clouds of individual trees for leaf-wood separation", <a href="https://doi.org/10.11588/data/UUMEDI">https://doi.org/10.11588/data/UUMEDI</a>, heiDATA, V1, UNF:6:9U7BGTgjjsWd1GduT1qXjA== [fileUNF]. Distributed under a Creative Commons Attribution 4.0 International Deed.</p> </div> <p>*For licensing reasons these data re not included in this repository but can be downladed from the doi provided. </p> <p> </p> <p> </p>
Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation
<p>A highly granular calorimeter, similar to CALICE is simulated in Geant4. The calorimeters showers are represented as either images or point clouds. Two state-of-the-art score based diffusion models, on image based and the other point cloud based, are trained on the same set of calorimeter simulations and directly compared to each other.<br> <br> These files include the original Geant4 simulation, an intermediate form of the data required for training, and the samples generated by the image and point cloud models.</p>
Point cloud Santa Maria de Vilagrassa Frieze
Nube de puntos Friso Santa Maria de Vilagrassa (Catalonia) - 13th century It is of Romanesque origin. The cover of the second half of 13th c., School of Lleida. In the medieval church remains the only Romanesque portal. This building already existed in the Visigothic period, was later refurbished and mixing with different styles. The square bell tower is the transition from Romanesque to Gothic, offers a particular perspective to their manifest inclination. Ever have called the Leaning Tower of Urgell." The year 1976 was declared a Cultural Asset of National Interest (BCIN) integrated into the artistic whole." 2020: Santa Maria de Vilagrassa - Photogrammetry - Terrestrial , Data Derivatives . Collected by Calidos . Distributed by Open Heritage 3D. https://doi.org/10.26301/37v3-j883 Source: Objaverse 1.0 / Sketchfab
Tyrannosaurus Rex - AMNH [Point Cloud]
"The 4-foot-long jaw, the 6-inch-long teeth, the massive thigh bones—almost everything about Tyrannosaurus rex indicates the enormous power of one of the largest theropod dinosaurs that ever existed. The fossil was originally arranged so that the dinosaur stood upright. Museum scientists later determined that it was more accurate to show the Tyrannosaurus rex mounted in a stalking position, with its head low, tail extended, and one foot slightly raised." ~ https://www.amnh.org/exhibitions/permanent-exhibitions/fossil-halls/hall-of-saurischian-dinosaurs/tyrannosaurus-rex Source: Objaverse 1.0 / Sketchfab
Amphoriskos - Raw Point Cloud
The pointcloud direct fromPython Photogrammetry Toolktit. pt 1 of post-processing. Source: Objaverse 1.0 / Sketchfab
Veloso Cave - 3D reconstruction v1 (point cloud)
This an initial 3D reconstruction of the Veloso Mine in Ouro Preto - Minas Gerais in Brasil. This reconstruction was performed with LiDar odometry and RGBD câmeras, using the Espeleorobô ver. II from the Instituto Tecnológico Vale (ITV). Source: Objaverse 1.0 / Sketchfab
Swahili Staff - point cloud
Top section of a 19th or 20th-century Swahili staff, now in the collection of the Minneapolis Institute of Art. This point cloud version was inspired by Sketchfab's #plantpointschallenge contest, but it's not a contest entry. More information about the object here: https://collections.artsmia.org/art/12117/staff-swahili The mesh version of this object is here on Sketchfab: https://skfb.ly/6qEvt Source: Objaverse 1.0 / Sketchfab
Point Cloud, Martha's Screen, Rosedown
A 3D scan of Martha's Screen at Rosedown Plantation in St. Francisville, Louisiana, USA as part of a project to document the house, landscape, and artifacts. **Artifact: **Martha's Screen **Location:** Rosedown Plantation, St. Francisville, Louisiana, USA **Team:** Brendan Harmon & Nicholas Serrano **Camera:** Nikon 5600 **Software:** Agisoft Metashape Source: Objaverse 1.0 / Sketchfab
Point Cloud-Medieval Hermitage of Oreto-Zuqueca
This point cloud of the medieval Hermitage of Nuestra Señora de Oreto-Zuqueca in Granátula de Calatrava (Ciudad Real, Spain) was collected using three FARO Focus3D scanners in 2017. The roof has been purposefully removed from the point cloud. The hermitage sits on one of the most important archaeological sites in Castilla-La Mancha region with occupations in Iberian, Roman, Visigothic and Islamic times. This settlement controlled the passage over the Jabalón River for centuries. The current hermitage was built in the 13th century, some years after the victory of the Christian troops in the battle of Las Navas de Tolosa in 1212. It was built with complete safety on the remains of a previous church. For its construction, a large number of stones from the site that extends under its feet were reused. Among these stones, the reuse of decorated pieces from the Visigothic period that can be seen today on its walls is especially significant. Source: Objaverse 1.0 / Sketchfab
Camden Lock, London - 3D Point Cloud (E,C)
Camden Lock, London, UK "Camden Lock is a small part of Camden Town, London Borough of Camden, England, which was formerly a wharf with stables on the Regent's Canal." 3D Point Cloud - Preview (External, Coloured) SIAD REF: OX116 Source: Objaverse 1.0 / Sketchfab
LIDAROGRAPHY / Low Point Cloud Bull
Testing SiteScape app scanning. Source: Objaverse 1.0 / Sketchfab
Desperate Dan Point Cloud
Source: Objaverse 1.0 / Sketchfab
Palmyra. Tower tomb of Elahbel | Point Cloud
American Colony . Photo Dept, photographer. [Palmyra. Tower tomb of Elahbel. Interior showing ceiling, pilasters and grave niches ; lower section showing crypt and carved busts]. Syria Tadmur, 1920. [Approximately to 1933] Photograph. Retrieved from the Library of Congress, https://www.loc.gov/item/mpc2004002205/PP/. (Accessed August 09, 2017.)  Source: Objaverse 1.0 / Sketchfab
Point Cloud-13
test-3 Source: Objaverse 1.0 / Sketchfab
Essex Castle, Alderney - Point cloud
Essai de visualisation d'un nuage de points extrait de Photoscan. Modélisé à partir d'une vidéo Youtube Source: Objaverse 1.0 / Sketchfab
Bois-Couturier Megalithic Tomb Point Cloud
Allée couverte du Bois-Couturier, a megalithic tomb located in Guiry-en-Vexin, France was discovered in 1915 by an agricultural worker. This ancient tomb is buried in the hillside overlooking the Aubette valley at an altitude of 132 meters. Facing south/ southeast, the burial chamber measures 7 meters long and 2 meters wide. The antechamber is short and wide measuring 1 meter by 1.85 meters. The walls, constructed of horizontal layers of limestone plates, measure 1.30 meters high. The arrangement of the limestone slabs of the roof have been disturbed, and their current position is a result of restoration. This Point Cloudwas created from FARO Focus S70 scan data and processed in FARO SCENE. For more information see: COSTA From SOULIER Ph. GUY H. 1995 - Dolmens et menhirs du Val-d'Oise, Notices d'archéologie du Val-d'Oise n°4, Service départemental d'archéologie du Val-d'Oise, p. 26-27 Source: Objaverse 1.0 / Sketchfab
Palmyra. Funereal sculpture | Point Cloud
American Colony . Photo Dept, photographer. Palmyra. Funereal sculpture. Six full length figures on sarcophagus. Syria Tadmur, 1920. [Approximately to 1933] Photograph. Retrieved from the Library of Congress, https://www.loc.gov/item/mpc2004002224/PP/. (Accessed August 05, 2017.)  Source: Objaverse 1.0 / Sketchfab
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.