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46
datasets available to search
ShareScore release 0.9.0
Dataset results
46 results for “RGBD”
VOT19 Challenge RGBD
<p>Dataset for the VOT19 RGBD Challenge.</p>
Generated RGBD Tracking Benchmarks (LaSOT) Part01
<p>We convert the existing RGB tracking benchmarks, eg. LaSOT, to RGB+pseudo-Depth tracking benchmarks using DenseDepth or HighResDepth monocular depth estimation methods.</p>
Generated RGBD Tracking Benchmarks (LaSOT) Part 04
<p>We convert the existing RGB tracking benchmarks, eg. LaSOT, to RGB+pseudo-Depth tracking benchmarks using DenseDepth or HighResDepth monocular depth estimation methods.</p>
Generated RGBD Tracking Benchmarks (LaSOT) Part 02
<p>We convert the existing RGB tracking benchmakrs, eg. LaSOT, to RGB+pseudo-Depth tracking benchmarks using DenseDepth or HighResDepth monocular estimation methods.</p>
Generated RGBD Tracking Benchmarks (LaSOT) Part 08
<p>We convert the existing RGB tracking benchmarks, eg. LaSOT, to RGB+pseudo-Depth tracking benchmarks using DenseDepth or HighResDepth monocular depth estimation methods.</p>
Generated RGBD Tracking Benchmarks (LaSOT) Part 07
<p>We convert the existing RGB tracking benchmarks, eg. LaSOT, to RGB+pseudo-Depth tracking benchmarks using DenseDepth or HighResDepth monocular depth estimation methods.</p>
Generated RGBD Tracking Benchmarks (LaSOT) Part 05
<p>We convert the existing RGB tracking benchmarks, eg. LaSOT, to RGB+pseudo-Depth tracking benchmarks using DenseDepth or HighResDepth monocular depth estimation methods.</p>
Generated RGBD Tracking Benchmarks (LaSOT) Part 06
<p>We convert the existing RGB tracking benchmarks, eg. LaSOT, to RGB+pseudo-Depth tracking benchmarks using DenseDepth or HighResDepth monocular depth estimation methods.</p>
Generated RGBD Tracking Benchmarks (LaSOT) Part13
<p>Fix the broken zip files, including:</p> <p>lion, kangaroo</p>
Generated RGBD Tracking Benchmarks (LaSOT) Part12
<p>Fix the broken zip files, including:</p> <p>lizard, microphone, monkey, motorcycle, person</p>
Generated RGBD Tracking Benchmarks (LaSOT) Part11
<p>Fixed broken zip files, including :</p> <p>pig, rabbit, robot, rubicCube</p>
Generated RGBD Tracking Benchmarks (Got10K) 1501-2100
<p>We convert the existing RGB tracking benchmakrs, eg. Got10K, to RGB+pseudo-Depth tracking benchmarks using DenseDepth or HighResDepth monocular estimation methods.</p> <p>Please visit the Got10K page for the RGB images and the groundtruths.</p>
Generated RGBD Tracking Benchmarks (Got10K) 1-700
<p>We convert the existing RGB tracking benchmakrs, eg. Got10K, to RGB+pseudo-Depth tracking benchmarks using DenseDepth or HighResDepth monocular estimation methods.</p> <p>Please visit the Got10K page for the RGB images and the groundtruths.</p>
Generated RGBD Tracking Benchmarks (Got10K) 6201-6400
<p>We convert the existing RGB tracking benchmakrs, eg. Got10K, to RGB+pseudo-Depth tracking benchmarks using DenseDepth or HighResDepth monocular estimation methods.</p> <p>Please visit the Got10K page for the RGB images and the groundtruths.</p>
Generated RGBD Tracking Benchmarks (LaSOT) Part 10
<p>We convert the existing RGB tracking benchmarks, eg. LaSOT, to RGB+pseudo-Depth tracking benchmarks using DenseDepth or HighResDepth monocular depth estimation methods.</p>
Generated RGBD Tracking Benchmarks (LaSOT) Part 09
<p>We convert the existing RGB tracking benchmarks, eg. LaSOT, to RGB+pseudo-Depth tracking benchmarks using DenseDepth or HighResDepth monocular depth estimation methods.</p>
HUMAN4D - Subject #3 (multi-RGBD + 2d/3d pose)
<p><strong>HUMAN4D: A Human-Centric Multimodal Dataset for Motions & Immersive Media (Subject #3)</strong></p> <p><strong>The dataset was captured with the use of VCL Volumetric Capture free software (<a href="https://github.com/VCL3D/VolumetricCapture">https://github.com/VCL3D/VolumetricCapture</a>) </strong></p> <ul> <li>device_repository.json includes the camera instrinsic parameters.</li> <li>pose.zip includes the camera extrinsic calibration parameters.</li> <li>offsets.zip include the frame offset between the pose ids (name_of_file==id) and the group frame ids of the RGBD data (first number before underscore in the filename of each file)</li> <li>S3_activities.txt files that maps the zip filenames with data for specific activities.</li> </ul> <p>HUMAN4D is a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric capture and an audio recording system. </p> <p>By capturing 2 female and 2 male professional actors performing various full-body movements and expressions, HUMAN4D provides a diverse set of motions and poses encountered as part of single- and multi-person daily, physical and social activities (jumping, dancing, etc.), along with multi-RGBD (mRGBD), volumetric and audio data.</p> <p>Despite the existence of multi-view color datasets captured with the use of hardware (HW) synchronization, to the best of our knowledge, HUMAN4D is the first and only public resource that provides volumetric depth maps with high synchronization precision due to the use of intra- and inter-sensor HW-SYNC. </p>
HUMAN4D - Subject #1 (multi-RGBD + 2d/3d pose)
<p><strong>HUMAN4D: A Human-Centric Multimodal Dataset for Motions & Immersive Media (Subject #1)</strong></p> <p><strong>The dataset was captured with the use of VCL Volumetric Capture free software (<a href="https://github.com/VCL3D/VolumetricCapture">https://github.com/VCL3D/VolumetricCapture</a>) </strong></p> <ul> <li>device_repository.json includes the camera instrinsic parameters.</li> <li>pose.zip includes the camera extrinsic calibration parameters.</li> <li>offsets.zip include the frame offset between the pose ids (name_of_file==id) and the group frame ids of the RGBD data (first number before underscore in the filename of each file)</li> <li>S1_activities.txt files that maps the zip filenames with data for specific activities.</li> </ul> <p>HUMAN4D is a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric capture and an audio recording system. </p> <p>By capturing 2 female and 2 male professional actors performing various full-body movements and expressions, HUMAN4D provides a diverse set of motions and poses encountered as part of single- and multi-person daily, physical and social activities (jumping, dancing, etc.), along with multi-RGBD (mRGBD), volumetric and audio data.</p> <p>Despite the existence of multi-view color datasets captured with the use of hardware (HW) synchronization, to the best of our knowledge, HUMAN4D is the first and only public resource that provides volumetric depth maps with high synchronization precision due to the use of intra- and inter-sensor HW-SYNC. </p>
HUMAN4D - Subject #2 (multi-RGBD + 2d/3d pose)
<p><strong>HUMAN4D: A Human-Centric Multimodal Dataset for Motions & Immersive Media (Subject #2)</strong></p> <p><strong>The dataset was captured with the use of VCL Volumetric Capture free software (<a href="https://github.com/VCL3D/VolumetricCapture">https://github.com/VCL3D/VolumetricCapture</a>) </strong></p> <ul> <li>device_repository.json includes the camera instrinsic parameters.</li> <li>pose.zip includes the camera extrinsic calibration parameters.</li> <li>offsets.zip include the frame offset between the pose ids (name_of_file==id) and the group frame ids of the RGBD data (first number before underscore in the filename of each file)</li> <li>S2_activities.txt files that maps the zip filenames with data for specific activities.</li> </ul> <p>HUMAN4D is a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric capture and an audio recording system. </p> <p>By capturing 2 female and 2 male professional actors performing various full-body movements and expressions, HUMAN4D provides a diverse set of motions and poses encountered as part of single- and multi-person daily, physical and social activities (jumping, dancing, etc.), along with multi-RGBD (mRGBD), volumetric and audio data.</p> <p>Despite the existence of multi-view color datasets captured with the use of hardware (HW) synchronization, to the best of our knowledge, HUMAN4D is the first and only public resource that provides volumetric depth maps with high synchronization precision due to the use of intra- and inter-sensor HW-SYNC. </p>
HUMAN4D - Subject #4 (multi-RGBD + 2d/3d pose)
<p><strong>HUMAN4D: A Human-Centric Multimodal Dataset for Motions & Immersive Media (Subject #4)</strong></p> <p><strong>The dataset was captured with the use of VCL Volumetric Capture free software (<a href="https://github.com/VCL3D/VolumetricCapture">https://github.com/VCL3D/VolumetricCapture</a>) </strong></p> <ul> <li>device_repository.json includes the camera instrinsic parameters.</li> <li>pose.zip includes the camera extrinsic calibration parameters.</li> <li>offsets.zip include the frame offset between the pose ids (name_of_file==id) and the group frame ids of the RGBD data (first number before underscore in the filename of each file)</li> <li>S4_activities.txt files that maps the zip filenames with data for specific activities.</li> </ul> <p>HUMAN4D is a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric capture and an audio recording system. </p> <p>By capturing 2 female and 2 male professional actors performing various full-body movements and expressions, HUMAN4D provides a diverse set of motions and poses encountered as part of single- and multi-person daily, physical and social activities (jumping, dancing, etc.), along with multi-RGBD (mRGBD), volumetric and audio data.</p> <p>Despite the existence of multi-view color datasets captured with the use of hardware (HW) synchronization, to the best of our knowledge, HUMAN4D is the first and only public resource that provides volumetric depth maps with high synchronization precision due to the use of intra- and inter-sensor HW-SYNC. </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.