383 machine learning datasets
383 dataset results
3D FRONT HUMAN is a dataset that extends the large-scale synthetic scene dataset 3D-FRONT. Specifically, the 3D scenes with humans, i.e., non-contact humans (a sequence of walking motion and standing humans) as well as contact humans (sitting, touching, and lying humans). 3D FRONT HUMAN contains four room types: 1) 5689 bedrooms, 2) 2987 living rooms, 3) 2549 dining rooms and 4) 679 libraries. We use 21 object categories for the bedrooms, 24 for the living and dining rooms, and 25 for the libraries.
Human Action Evaluation (HAE) has rarely been applied to real-world disease monitoring, the EHE dataset aims to gather sample data to validate effective HAE methods that could then be expanded on a larger validation scale. EHE consists of several actions from morning exercises that patients complete daily in the elderly home. The EHE dataset contained 869 action repetitions performed by 25 older people. Six exercises were collected for the EHE dataset via Kinect v2.
ContactArt is a dataset for learning hand-object interaction priors for hand and articulated object pose estimation. The dataset is created using visual teleoperation, where the human operator can directly play within a physical simulator to manipulate the articulated objects. All the object models are from Partnet dataset for the convenience of scaling up. ContactArt can provide accurate annotation, rich hand-object interaction, and contact information.
FaMoS is a dynamic 3D head dataset from 95 subjects, each performing 28 motion sequences. The sequences comprise of six prototypical expressions (i.e., Anger, Disgust, Fear, Happiness, Sadness, and Surprise), two head rotations (left/right and up/down), and diverse facial motions, including extreme and asymmetric expressions. Each sequence is recorded at 60 fps. In total, FaMoS contains around 600K 3D head meshes (i.e., ~225 frames per sequence). For each frame, registrations in FLAME meshes are publicly available.
Estimating camera motion in deformable scenes poses a complex and open research challenge. Most existing non-rigid structure from motion techniques assume to observe also static scene parts besides deforming scene parts in order to establish an anchoring reference. However, this assumption does not hold true in certain relevant application cases such as endoscopies. To tackle this issue with a common benchmark, we introduce the Drunkard’s Dataset, a challenging collection of synthetic data targeting visual navigation and reconstruction in deformable environments. This dataset is the first large set of exploratory camera trajectories with ground truth inside 3D scenes where every surface exhibits non-rigid deformations over time. Simulations in realistic 3D buildings lets us obtain a vast amount of data and ground truth labels, including camera poses, RGB images and depth, optical flow and normal maps at high resolution and quality.
Homepage | GitHub
Synthetic dataset of over 13,000 images of damaged and intact parcels with full 2D and 3D annotations in the COCO format. For details see our paper and for visual samples our project page.
A data set introduced for training on the protein design task.
3DYoga90 is organized within a three-level label hierarchy. It stands out as one of the most comprehensive open datasets, featuring the largest collection of RGB videos and 3D skeleton sequences among publicly available resources.
Depth vision has been recently used in many locomotion devices with the objective to ease the life of disabled people toward reaching more ecological lifestyle. This is due to the fact that such cameras are cheap, compact and can provide rich information about the environment. Our dataset provides many recordings of point cloud and other types of data during different locomotion modes in urban context. If you used this data, please cite the following papers below: 1-Depth Vision based Terrain Detection Algorithm during Human Locomotion 2-Using Depth Vision for Terrain Detection during Active Locomotion
High Quality Indoor Monocular Depth Estimation Dataset with focus on performance variation across space type
The Online Action Detection Dataset (OAD) was captured using the Kinect V2 sensor, which collects color images, depth images and human skeleton joints synchronously. This dataset includes 59 long sequences and 10 actions.
The ULS23 test set contains 725 lesions from 284 patients of the Radboudumc and JBZ hospitals in the Netherlands. It is intended to be used to measure the performance of 3D universal lesion segmentation models for Computed Tomography (CT). To prepare the data, radiological reports from both participating institutions where searched using NLP tools identifying patients with measurable target lesions, indicating that these lesions were clinically relevant. A random sample of patients was selected, 56.3% of which were male and with diverse scanner manufacturers. The lesions were annotated in 3D by expert radiologists with over 10 years of experience in reading oncological scans. ULS23 is an open benchmark, and we invite ongoing submissions to advance the development of future ULS models.
The National Lung Screening Trial (NLST) was a randomized controlled trial conducted by the Lung Screening Study group (LSS) and the American College of Radiology Imaging Network (ACRIN) to determine whether screening for lung cancer with low-dose helical computed tomography (CT) reduces mortality from lung cancer in high-risk individuals relative to screening with chest radiography. Approximately 54,000 participants were enrolled between August 2002 and April 2004. Data collection has ended, and information is complete through December 31, 2009. NLST has the ClinicalTrials.gov registration number NCT00047385.
Background: Lung cancer risk classification is an increasingly important area of research as low-dose thoracic CT screening programs have become standard of care for patients at high risk for lung cancer. There is limited availability of large, annotated public databases for the training and testing of algorithms for lung nodule classification.
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
This dataset comprehends the 3D building information model (in IFC and Revit formats), manually elaborated based on the terrestrial laser scanner of the sequence 2 of ConSLAM, and the refined ground truth (GT) poses (in TUM format) of sessions 2, 3, 4, and 5 of the open-access ConSLAM dataset (which provides camera, LiDAR, and IMU measurements).
The ARKitFace dataset is established by this work in order to train and evaluate both 3D face shape and 6DoF in the setting of perspective projection. A total of 500 volunteers, aged 9 to 60, are invited to record the dataset. They sit in a random environment, and the 3D acquisition equipment is fixed in front of them, with a distance ranging from about 0.3m to 0.9m. Each subject is asked to perform 33 specific expressions with two head movements (from looking left to looking right / from looking up to looking down). 3D acquisition equipment we used is an iPhone 11. The shape and location of human face are tracked by structured light sensor. The triangle mesh and 6DoF information of the RGB images are obtained by built-in ARKit toolbox. The triangle mesh is made up of 1,220 vertices and 2,304 triangles. In total, 902,724 2D facial images (resolution 1280×720 or 1440×1280) with ground-truth 3D mesh and 6DoF pose annotation are collected.
Lifespan HCP Release 2.0 includes cross-sectional visit 1 (V1) preprocessed structural and functional imaging data, unprocessed V1 imaging data for all included modalities (structural, high-res hippocampal T2, resting state fMRI, task fMRI, diffusion, and ASL), and non-imaging demographic and behavioral assessment data from 725 HCP-Aging (HCP-A, ages 36-100+) healthy participants (22+ TB of data).
UK Biobank participants have generously provided a very wide range of information about their health and well-being since recruitment began in 2006. This has been added to in the following ways: