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264
datasets available to search
ShareScore release 0.9.0
Dataset results
264 results for “3D Reconstruction”
Nasal Reconstruction Using a Customized 3D-printed Nasal Stent for Congenital Arhinia
ClinicalTrials.gov study NCT02559050. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Applicability of 3D Printing and 3D Digital Image Reconstruction in the Planning of Complex Liver Surgery (LIV3DPRINT).
ClinicalTrials.gov study NCT03416387. IPD Sharing: YES. Countries: 1. Publications: 0.
Use of an Interactive 3D Tool During Consultation for Breast Reconstruction Surgery
ClinicalTrials.gov study NCT05025020. IPD Sharing: YES. Countries: 1. Publications: 0.
Effect of 3D-printed Reconstruction System in Size of Left DLT
ClinicalTrials.gov study NCT06258954. IPD Sharing: NO. Countries: 0. Publications: 1.
Data from: Three-dimensional reconstructions come to life – interactive 3D PDF animations in functional morphology
Open the record for dataset details and reuse information.
Experimental method for 3D reconstruction of Odonata wings (methodology and dataset)
Open the record for dataset details and reuse information.
Data from: 3D sorghum reconstructions from depth images identify QTL regulating shoot architecture
Open the record for dataset details and reuse information.
Data from: A versatile pipeline for the multi-scale digital reconstruction and quantitative analysis of 3D tissue architecture
Open the record for dataset details and reuse information.
3D reconstruction of the mouse cochlea from scRNA-seq data suggests morphogen-based principles in apex-to-base specification
GEO Series GSE202588. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Cropped 3D volume of the reconstructed dataset from i23 beamline, DLS
<p>This is a cropped volume of the tomographic dataset obtained at i23 beamline of Diamond Light Source, UK</p>
Armour from Gammertingen: 3D reconstruction
The 3D model presents a digital reconstruction of solid and riveted rings of archaeological mail armour. Location: Landesmuseum Württemberg https://www.landesmuseum-stuttgart.de/ https://sketchfab.com/lmwstuttgart To create 3D models of the rings, we used polygonal modelling functions of Blender software. In order to record the relevant measurements of the mail rings, we used a list of parameters. This list includes 11 parameters for a riveted ring and four parameters for a solid ring. The accuracy of the digital replicas of the rings was 0.01 mm. The authors of the reconstructions are Martijn A. Wijnhoven (VU University Amsterdam) https://vu-nl.academia.edu/MartijnAWijnhoven Aleksei Moskvin (Saint Petersburg State University of Industrial Technologies and Design) https://independent.academia.edu/AlekseiMoskvin Mariia Moskvina (Saint Petersburg State University of Industrial Technologies and Design) https://independent.academia.edu/MariiaMoskvina DOI: https://doi.org/10.13140/RG.2.2.19005.38884 Source: Objaverse 1.0 / Sketchfab
MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis (Kinect Part 1)
<p>Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism. However, there exists a knowledge gap in how to apply geometric reconstruction and photorealism modeling (novel view synthesis) in a unified framework.</p><p>To address this gap and promote the development of robust and immersive modeling and rendering with consumer-grade devices, first, we propose a real-world Multi-Sensor Hybrid Room Dataset (MuSHRoom). Our dataset presents exciting challenges and requires state-of-the-art methods to be cost-effective, robust to noisy data and devices, and can jointly learn 3D reconstruction and novel view synthesis, instead of treating them as separate tasks, making them ideal for real-world applications. Second, we benchmark several famous pipelines on our dataset for joint 3D mesh reconstruction and novel view synthesis. Finally, in order to further improve the overall performance, we propose a new method that achieves a good trade-off between the two tasks. Our dataset and benchmark show great potential in promoting the improvements for fusing 3D reconstruction and high-quality rendering in a robust and computationally efficient end-to-end fashion.</p>
MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis (Kinect Part 2)
<p>Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism. However, there exists a knowledge gap in how to apply geometric reconstruction and photorealism modeling (novel view synthesis) in a unified framework.</p><p>To address this gap and promote the development of robust and immersive modeling and rendering with consumer-grade devices, first, we propose a real-world Multi-Sensor Hybrid Room Dataset (MuSHRoom). Our dataset presents exciting challenges and requires state-of-the-art methods to be cost-effective, robust to noisy data and devices, and can jointly learn 3D reconstruction and novel view synthesis, instead of treating them as separate tasks, making them ideal for real-world applications. Second, we benchmark several famous pipelines on our dataset for joint 3D mesh reconstruction and novel view synthesis. Finally, in order to further improve the overall performance, we propose a new method that achieves a good trade-off between the two tasks. Our dataset and benchmark show great potential in promoting the improvements for fusing 3D reconstruction and high-quality rendering in a robust and computationally efficient end-to-end fashion.</p>
MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis (Kinect Part 3)
<p>Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism. However, there exists a knowledge gap in how to apply geometric reconstruction and photorealism modeling (novel view synthesis) in a unified framework.</p><p>To address this gap and promote the development of robust and immersive modeling and rendering with consumer-grade devices, first, we propose a real-world Multi-Sensor Hybrid Room Dataset (MuSHRoom). Our dataset presents exciting challenges and requires state-of-the-art methods to be cost-effective, robust to noisy data and devices, and can jointly learn 3D reconstruction and novel view synthesis, instead of treating them as separate tasks, making them ideal for real-world applications. Second, we benchmark several famous pipelines on our dataset for joint 3D mesh reconstruction and novel view synthesis. Finally, in order to further improve the overall performance, we propose a new method that achieves a good trade-off between the two tasks. Our dataset and benchmark show great potential in promoting the improvements for fusing 3D reconstruction and high-quality rendering in a robust and computationally efficient end-to-end fashion.</p>
MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis (Kinect Part 4)
<p>Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism. However, there exists a knowledge gap in how to apply geometric reconstruction and photorealism modeling (novel view synthesis) in a unified framework.</p><p>To address this gap and promote the development of robust and immersive modeling and rendering with consumer-grade devices, first, we propose a real-world Multi-Sensor Hybrid Room Dataset (MuSHRoom). Our dataset presents exciting challenges and requires state-of-the-art methods to be cost-effective, robust to noisy data and devices, and can jointly learn 3D reconstruction and novel view synthesis, instead of treating them as separate tasks, making them ideal for real-world applications. Second, we benchmark several famous pipelines on our dataset for joint 3D mesh reconstruction and novel view synthesis. Finally, in order to further improve the overall performance, we propose a new method that achieves a good trade-off between the two tasks. Our dataset and benchmark show great potential in promoting the improvements for fusing 3D reconstruction and high-quality rendering in a robust and computationally efficient end-to-end fashion.</p>
MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis (iPhone Part 2)
<p>Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism. However, there exists a knowledge gap in how to apply geometric reconstruction and photorealism modeling (novel view synthesis) in a unified framework.</p><p>To address this gap and promote the development of robust and immersive modeling and rendering with consumer-grade devices, first, we propose a real-world Multi-Sensor Hybrid Room Dataset (MuSHRoom). Our dataset presents exciting challenges and requires state-of-the-art methods to be cost-effective, robust to noisy data and devices, and can jointly learn 3D reconstruction and novel view synthesis, instead of treating them as separate tasks, making them ideal for real-world applications. Second, we benchmark several famous pipelines on our dataset for joint 3D mesh reconstruction and novel view synthesis. Finally, in order to further improve the overall performance, we propose a new method that achieves a good trade-off between the two tasks. Our dataset and benchmark show great potential in promoting the improvements for fusing 3D reconstruction and high-quality rendering in a robust and computationally efficient end-to-end fashion.</p>
MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis (iPhone Part 1)
<p>Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism. However, there exists a knowledge gap in how to apply geometric reconstruction and photorealism modeling (novel view synthesis) in a unified framework.</p><p>To address this gap and promote the development of robust and immersive modeling and rendering with consumer-grade devices, first, we propose a real-world Multi-Sensor Hybrid Room Dataset (MuSHRoom). Our dataset presents exciting challenges and requires state-of-the-art methods to be cost-effective, robust to noisy data and devices, and can jointly learn 3D reconstruction and novel view synthesis, instead of treating them as separate tasks, making them ideal for real-world applications. Second, we benchmark several famous pipelines on our dataset for joint 3D mesh reconstruction and novel view synthesis. Finally, in order to further improve the overall performance, we propose a new method that achieves a good trade-off between the two tasks. Our dataset and benchmark show great potential in promoting the improvements for fusing 3D reconstruction and high-quality rendering in a robust and computationally efficient end-to-end fashion.</p>
MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis (iPhone Part 3)
<p>Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism. However, there exists a knowledge gap in how to apply geometric reconstruction and photorealism modeling (novel view synthesis) in a unified framework.</p><p>To address this gap and promote the development of robust and immersive modeling and rendering with consumer-grade devices, first, we propose a real-world Multi-Sensor Hybrid Room Dataset (MuSHRoom). Our dataset presents exciting challenges and requires state-of-the-art methods to be cost-effective, robust to noisy data and devices, and can jointly learn 3D reconstruction and novel view synthesis, instead of treating them as separate tasks, making them ideal for real-world applications. Second, we benchmark several famous pipelines on our dataset for joint 3D mesh reconstruction and novel view synthesis. Finally, in order to further improve the overall performance, we propose a new method that achieves a good trade-off between the two tasks. Our dataset and benchmark show great potential in promoting the improvements for fusing 3D reconstruction and high-quality rendering in a robust and computationally efficient end-to-end fashion.</p>
Vimose coat mail fabric: 3D reconstruction
The 3D model presents a digital reconstruction of archaeological mail fabric. To create 3D models of the rings, we used polygonal modelling functions of Blender software. In order to record the relevant measurements of the mail rings, we used a list of parameters. This list includes 11 parameters for a riveted ring and four parameters for a solid ring. The accuracy of the digital replicas of the rings was 0.01 mm. Subsequently the specimen of mail was tested in VR to reveal its physical and mechanical properties. For further details see https://doi.org/10.1016/j.culher.2020.12.002 The authors of the reconstruction are Martijn A. Wijnhoven https://vu-nl.academia.edu/MartijnAWijnhoven (VU University Amsterdam) Aleksei Moskvin https://independent.academia.edu/AlekseiMoskvin https://sketchfab.com/alekseimoskvin1/ Mariia Moskvina https://independent.academia.edu/MariiaMoskvina https://sketchfab.com/mariia89 (Saint Petersburg State University of Industrial Technologies and Design) Source: Objaverse 1.0 / Sketchfab
Vimose belt mail fabric: 3D reconstruction
The 3D model presents a digital reconstruction of archaeological mail fabric. To create 3D models of the rings, we used polygonal modelling functions of Blender software. In order to record the relevant measurements of the mail rings, we used a list of parameters. This list includes 11 parameters for a riveted ring and four parameters for a solid ring. The accuracy of the digital replicas of the rings was 0.01 mm. Subsequently the specimen of mail was tested in VR to reveal its physical and mechanical properties. For further details see https://doi.org/10.1016/j.culher.2020.12.002 The authors of the reconstruction are Aleksei Moskvin https://independent.academia.edu/AlekseiMoskvin https://sketchfab.com/alekseimoskvin1/ Mariia Moskvina https://independent.academia.edu/MariiaMoskvina https://sketchfab.com/mariia89 (Saint Petersburg State University of Industrial Technologies and Design) Martijn A. Wijnhoven https://vu-nl.academia.edu/MartijnAWijnhoven (VU University Amsterdam) 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.