The datasets includes curves drawn on 3D surfaces (triangle meshes) in Virtual Reality. A total of 2,880 curves were created using two different techniques by 20 users on 6 meshes. For each curve, a 3D curve executed by the user is provided, the projected curve created on the mesh, and the ground truth target curve on the mesh. For collecting the data, two different task types were employed, which are described in the paper.
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VesselGraph is a dataset of whole-brain vessel graphs based on specific imaging protocols. Specifically, vascular graphs are extracted using a refined graph extraction scheme leveraging the volume rendering engine Voreen and provided in an accessible and adaptable form through the OGB and PyTorch Geometric dataloaders.
XA Bin-Picking is a point-cloud dataset comprising both simulated and real-world scenes with three industrial parts. Synthesized scenes consists of 1000 training samples. The test samples are real scenes and the ground truth instance labels are made manually. There are 20 to 30 identical types of parts randomly piled up in a scene. Each scene contains about 60,000 boundary points. Each point in the scene has instance annotations. The parts are texture-less and have no discernible color. Both of training samples and test sam- ples only contain the boundary points of parts.
aiMotive dataset is a multimodal dataset for robust autonomous driving with long-range perception. The dataset consists of 176 scenes with synchronized and calibrated LiDAR, camera, and radar sensors covering a 360-degree field of view. The collected data was captured in highway, urban, and suburban areas during daytime, night, and rain and is annotated with 3D bounding boxes with consistent identifiers across frames.
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A cross-city UDA benchmark built upon nuScenes.
Description: 4,458 People - 3D Facial Expressions Recognition Data. The collection scenes include indoor scenes and outdoor scenes. The dataset includes males and females. The age distribution ranges from juvenile to the elderly, the young people and the middle aged are the majorities. The device includes iPhone X, iPhone XR. The data diversity includes different expressions, different ages, different races, different collecting scenes. This data can be used for tasks such as 3D facial expression recognition.
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The ASL-Skeleton3D introduces a representation based on mapping into the three-dimensional space the coordinates of the signers in the ASLLVD dataset. This enables a more accurate observation of the body parts and the signs articulation, allowing researchers to better understand the language and explore other approaches to the SLR field.
Reflectance measurements of Bidirectional Texture Functions (BTFs)
The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by XRE nvand located in the FleX-ray Lab at the Centrum Wiskunde & Informatica (CWI) in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another.
The dataset collected at the University of Florence during 2012, has been captured using a Kinect camera. It includes 9 activities: wave, drink from a bottle, answer phone,clap, tight lace, sit down, stand up, read watch, bow. During acquisition, 10 subjects were asked to perform the above actions for 2/3 times. This resulted in a total of 215 activity samples.
The volumetric representation of human interactions is one of the fundamental domains in the development of immersive media productions and telecommunication applications. Particularly in the context of the rapid advancement of Extended Reality (XR) applications, this volumetric data has proven to be an essential technology for future XR elaboration. In this work, we present a new multimodal database to help advance the development of immersive technologies. Our proposed database provides ethically compliant and diverse volumetric data, in particular 27 participants displaying posed facial expressions and subtle body movements while speaking, plus 11 participants wearing head-mounted displays (HMDs). The recording system consists of a volumetric capture (VoCap) studio, including 31 synchronized modules with 62 RGB cameras and 31 depth cameras. In addition to textured meshes, point clouds, and multi-view RGB-D data, we use one Lytro Illum camera for providing light field (LF) data simul
H3D (Humans in 3D) is a dataset of annotated people. The annotations include:
IKEA 3D is a dataset of IKEA 3D models and aligned images, which is suitable for pose estimation. There are 759 images and 219 models including Sketchup (skp) and Wavefront (obj) files.
InfiniteRep is a synthetic, open-source dataset for fitness and physical therapy (PT) applications. It includes 1k videos of diverse avatars performing multiple repetitions of common exercises. It includes significant variation in the environment, lighting conditions, avatar demographics, and movement trajectories. From cadence to kinematic trajectory, each rep is done slightly differently -- just like real humans. InfiniteRep videos are accompanied by a rich set of pixel-perfect labels and annotations, including frame-specific repetition counts.
OpenSurfaces is a large database of annotated surfaces created from real-world consumer photographs. The framework used for the annotation process draws on crowdsourcing to segment surfaces from photos, and then annotate them with rich surface properties, including material, texture and contextual information.
The Princeton Shape dataset provides a repository of 3D models and software tools for evaluating shape-based retrieval and analysis algorithms. The motivation is to promote the use of standardized data sets and evaluation methods for research in matching, classification, clustering, and recognition of 3D models. Researchers are encouraged to use these resources to produce comparisons of competing algorithms in future publications. There are 1,814 models in total.
Human activity recognition and clinical biomechanics are challenging problems in physical telerehabilitation medicine. However, most publicly available datasets on human body movements cannot be used to study both problems in an out-of-the-lab movement acquisition setting. The objective of the VIDIMU dataset is to pave the way towards affordable patient tracking solutions for remote daily life activities recognition and kinematic analysis.