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3 results for “Neural Radiance Fields”
ThermoScenes: Multimodal Neural Radiance Fields for Thermal Novel View Synthesis
<p>Thermal+RGB dataset for ThermoNeRF</p>
FruitNeRF: A Unified Neural Radiance Field based Fruit Counting Framework
<p>We introduce FruitNeRF, a unified novel fruit counting framework that leverages state-of-the-art view synthesis methods to count any fruit type directly in 3D. Our framework takes an unordered set of posed images captured by a monocular camera and segments fruit in each image. To make our system independent of the fruit type, we employ a foundation model that generates binary segmentation masks for any fruit. Utilizing both modalities, RGB and semantic, we train a semantic neural radiance field. Through uniform volume sampling of the implicit Fruit Field, we obtain fruit-only point clouds. By applying cascaded clustering on the extracted point cloud, our approach achieves precise fruit count. The use of neural radiance fields provides significant advantages over conventional methods such as object tracking or optical flow, as the counting itself is lifted into 3D. Our method prevents double counting fruit and avoids counting irrelevant fruit. We evaluate our methodology using both real-world and synthetic datasets. The real-world dataset consists of three apple trees with manually counted ground truths, a benchmark apple dataset with one row and ground truth fruit location, while the synthetic dataset comprises various fruit types including apple, plum, lemon, pear, peach, and mangoes. Additionally, we assess the performance of fruit counting using the foundation model compared to a U-Net. </p>
Shadow Neural Radiance Fields for Multi-View Satellite Photogrammetry - Dataset
<p>Data accompanying the paper Derksen, Dawa, and Dario Izzo. "Shadow Neural Radiance Fields for Multi-view Satellite Photogrammetry." <em>Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition</em>. 2021.</p> <p>This dataset contains a subset of the World-View-3 images from the IEEE Data Fusion Competition 2019 - Track 3, downloaded from https://ieee-dataport.org/open-access/data-fusion-contest-2019-dfc2019.</p> <p>It is organized in four folders, one for each study area, following the original area names. Each folder contains a multi-view set of RGB images, cropped to the validation area, and rotated according to the azimuth angle. The folder also contains a Digital Surface Model of the area (DSM) as a one-band .tif file where the values contained in pixels represent the surface altitude in meters. Finally the folder contains a "metadata" file which provides for each image ID the radius (distance from satellite to scene), as well as the viewing and lighting directions (azimuth and elevation).</p> <p>The authors would like to thank the Johns Hopkins University Applied Physics Laboratory and IARPA for providing the data used in this study, and the IEEE GRSS Image Analysis and Data Fusion Technical Committee for organizing the Data Fusion Contest.</p>
ScienceDex guides
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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.
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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.