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222 results for “point cloud”
Final point cloud results of SPC-MVSNet on DTU dataset
<p>SPC-MVSNet在DTU数据集上的最终点云结果</p>
Final point cloud results of SPC-MVSNet on Tanks and Temples benchmark
<p>SPC-MVSNet在坦克和寺庙基准上的最终点云结果。</p>
Multiway Registration Capability Study in Increasing the Accuracy of Registration Results for Infrastructure and Mining Pits Terrestrial Laser Scanner (TLS) Data Point Cloud
<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</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>
QAVA-DPC: Eye-Tracking Based Quality Assessment and Visual Attention Dataset for Dynamic Point Cloud in 6 DoF
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Point cloud from terrestrial laser scanning of a coppiced beech tree (Fagus sylvatica) in Wytham Woods, UK
<p>Point cloud of an old beech coppice stool located in Wytham Woods, UK. </p> <p>A 3D model of the tree can be found <a href="https://skfb.ly/6WLqp">here</a> on SketchFab.</p> <p>Data was captured on 20/4/2021 with a RIEGL VZ-400 terrestrial laser scanner from 6 scan locations around the tree. The weather was good, with little to no noticeable wind. Data is a binary PLY format with reflectance, deviation and PCV fields as well as RGB. Note this data set still includes some ground returns.</p> <p>Please acknowledge the data set authors if using this data.</p>
Points clouds of the best Somma-Vesuvius gravitative deformation process analog model
<p>Dense points clouds of the best Somma-Vesuvius gravitative deformation process analog model, realized with the Agisoft Photoscan software using a dataset of photos taken with four different cameras.</p>
PointFaces: Point Cloud Dataset of Facial Landmarks and Blendshapes
<p>The dataset is based on 1000 facial landmark captures performed on the FFHQ facial image dataset.<br> The 3D blendshape models are sculpted based on the MediaPipe canonical model.</p>
Data for: UK redwoods terrestrial laser scanner point clouds
<p class="MsoNormal">Giant redwoods (<em>Sequoiadendron giganteum) </em>are some of the UK's largest trees, despite only being introduced in the mid-19th century. Given recent interest in planting redwoods in the UK, partly due to their carbon sequestration potential and also their undoubted public appeal, an understanding of their viability is important. However, little or no research has been conducted to quantitatively estimate their carbon uptake in UK conditions. We used 3D terrestrial laser scanning (TLS) to make detailed structure measurements of individual <em>S. giganteum</em> trees at three sites, to estimate aboveground biomass (AGB) and annual biomass accumulation rates. We show that UK-grown <em>S. giganteum</em> can sequester carbon at a rate of 80 - 100 kg C year<sup>-1</sup>, varying with climate, management and age. This accumulation rate is 2.5 and 20 times faster than commonly-grown UK plantation tree species. We develop new UK-specific allometric models for <em>S. giganteum</em>which fit observed AGB with r<sup>2</sup> > 0.93 and bias < 2% and can be used to estimate <em>S. giganteum</em> AGB more generally. <em>S. giganteum</em> appears to represent a small but potentially important addition to the UK's carbon sequestration efforts and this work provides a baseline for estimating their longer term AGB and carbon sequestration capacity.</p>
Blast Furnace Raw Material Granularity Recognition Model Based on Deep Learning and Multimodal Fusion of 3D Point Cloud
<p>Provide data code</p>
Developing a Method to Automatically Extract Road Boundary and Linear Road Markings from MMS Point Cloud using OBB Collision Detection Techniques
<p>This video demonstrates the application of our method in a software tool for constructing road boundaries and lane marking data.</p>
Ananias Chapel, Syria Point Cloud
Lidar data, clean,edit, and uniform The Chapel of Saint Ananias, also known as the House of Saint Ananias, is a small underground chapel located in the ancient city of Damascus, Syria. The site is connected to the biblical story of Jesus's disciple, Saul of Tarsus, later known as St. Paul the Apostle. Over many centuries, non-Christian rulers have continually destroyed the chapel, rebuilt as a pagan temple and mosque before Franciscans rebuilt it in 1814. More info: https://doi.org/10.26301/j4g0-ek82 Cyark 2019 Ananias Chapel - LiDAR - Terrestrial . Collected by Directorate General of Antiquities and Museums . Distrubuted by Open Heritage 3D. Source: Objaverse 1.0 / Sketchfab
Mapping scrub vegetation cover from photogrammetric point-clouds
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UAV-LiDAR point clouds from the Forest and Biodiversity Experiment 2 (2022)
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Plot-level wood-leaf separation for terrestrial laser scanning point clouds
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Data for: UK redwoods terrestrial laser scanner point clouds
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Schooner 02: 25mm Point Cloud
Here's another test - the point cloud filtered to 25mm spacing Source: Objaverse 1.0 / Sketchfab
Golden Gate Bridge - Point Cloud Portrait
Point cloud obtained from drone video Source: Objaverse 1.0 / Sketchfab
A Dynamic Points Removal Benchmark in Point Cloud Maps
<p>Uniformat Dataset LiDAR Point Cloud Data [PCD format: with pose and points (x,y,z,/i)] <br>We recommend you read this wiki page first to know about data: <a href="https://kth-rpl.github.io/DynamicMap_Benchmark/data/">https://kth-rpl.github.io/DynamicMap_Benchmark/data/</a><br>For methods detail: <a href="https://github.com/KTH-RPL/DynamicMap_Benchmark">DynamicMap_Benchmark repo</a>, <a href="https://github.com/KTH-RPL/dufomap">DUFOMap</a>, <a href="https://github.com/MKJia/BeautyMap">BeautyMap</a>.</p> <ul> <li>00: <a href="https://www.cvlibs.net/datasets/kitti/index.php">KITTI sequence</a> 00 [VLP-64] from frame 4390 to 4530</li> <li>05: <a href="https://www.cvlibs.net/datasets/kitti/index.php">KITTI sequence</a> 05 [VLP-64] from frame 2350 to 2670</li> <li>av2: <a href="https://www.argoverse.org/av2.html">Argoverse 2.0</a> one sequence on <em>07YOTznatmYypvQYpzviEcU3yGPsyaGg__Spring_2020. </em>[2 x VLP-32]</li> <li>KTH campus: <a href="https://mcdviral.github.io/">CVPR'24 MCD</a>(Leica RTC360) For convenience teaser running, we include only 18 frames in data. Please check <a href="https://mcdviral.github.io/">the MCD project page</a> for more.</li> <li>semindoor: semi-indoor dataset collected by [VLP-16], collected by ourselves. </li> <li>twofloor: complex structure with two floors[Livox mid-360], collected by ourselves.</li> </ul> <p> </p> <table> <tbody> <tr> <td>Dataset</td> <td>Description</td> <td>Sensor Type</td> <td>Total Frame Number</td> </tr> <tr> <td>KITTI sequence 00</td> <td>in a small town with few dynamics (including one pedestrian around)</td> <td>VLP-64</td> <td>141</td> </tr> <tr> <td>KITTI sequence 05</td> <td>in a small town straight way, one higher car, the benchmarking paper cover image from this sequence.</td> <td>VLP-64</td> <td>321</td> </tr> <tr> <td>Argoverse2</td> <td>in a big city, crowded and tall buildings (including cyclists, vehicles, people walking near the building etc.</td> <td>2 x VLP-32</td> <td>575</td> </tr> <tr> <td>KTH campus (no gt)</td> <td>Collected by us (Thien-Minh) on the KTH campus. Lots of people move around on the campus. The DUFOMap paper cover image is from this one.</td> <td>Leica RTC360</td> <td>18</td> </tr> <tr> <td>Semi-indoor</td> <td>Collected by us (Qingwen & Mingkai), running on a small 1x2 vehicle with two people walking around the platform.</td> <td>VLP-16</td> <td>960</td> </tr> <tr> <td>Twofloor (no gt)</td> <td>Collected by us (Bowen Yang) in a quadruped robot. A two-floor structure environment with one pedestrian around.</td> <td>Livox-mid 360</td> <td>3305</td> </tr> </tbody> </table> <p> </p> <p>Cite as:</p> <blockquote> <p>@inproceedings{zhang2023benchmark,<br> author={Zhang, Qingwen and Duberg, Daniel and Geng, Ruoyu and Jia, Mingkai and Wang, Lujia and Jensfelt, Patric},<br> booktitle={IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)}, <br> title={A Dynamic Points Removal Benchmark in Point Cloud Maps}, <br> year={2023},<br> pages={608-614},<br> doi={10.1109/ITSC57777.2023.10422094}<br>}<br>@article{jia2024beautymap,<br> author={Jia, Mingkai and Zhang, Qingwen and Yang, Bowen and Wu, Jin and Liu, Ming and Jensfelt, Patric},<br> journal={IEEE Robotics and Automation Letters}, <br> title={{BeautyMap}: Binary-Encoded Adaptable Ground Matrix for Dynamic Points Removal in Global Maps}, <br> year={2024},<br> volume={9},<br> number={7},<br> pages={6256-6263},<br> doi={10.1109/LRA.2024.3402625}<br>}<br>@article{daniel2024dufomap,<br> author={Duberg, Daniel and Zhang, Qingwen and Jia, Mingkai and Jensfelt, Patric},<br> journal={IEEE Robotics and Automation Letters}, <br> title={{DUFOMap}: Efficient Dynamic Awareness Mapping}, <br> year={2024},<br> volume={9},<br> number={6},<br> pages={5038-5045},<br> doi={10.1109/LRA.2024.3387658}<br>}</p> </blockquote> <p> </p> <p>If you use this data, feel free to add your project to <a href="https://kth-rpl.github.io/DynamicMap_Benchmark/papers/">https://kth-rpl.github.io/DynamicMap_Benchmark/papers/</a></p>
Original point cloud from laser scanning of the Kladno railway station
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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)
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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.