3D Object Detection

This is a dataset of robot motions based on physics simulations. Provide: * a high-level explanation of the dataset characteristics * explain motivations and summary of its content * potential use cases of the dataset Note: * Training Input: $\alpha$ (yaw angle) and $\beta$ (pitch angle) * Traning output: $L_1$, $L_2$, and $L_3$ (length variations of each tendon)

MVX incorporates realistic physical world simulation with a differentiable accurate ray tracing wireless simulation that includes multi-agent and multimodal datasets for AI-driven digital twin applications in vehicular communication systems.

This dataset includes 3D point-cloud and 2D imagery from a flash LiDAR...

The SimBEV dataset is a collection of 320 scenes spread across all 11 CARLA maps and contains data from a variety of sensors, including five camera types (RGB, semantic segmentation, instance segmentation, depth, and optical flow), lidar, semantic lidar, radar, GNSS, and IMU, along with 3D object bounding boxes and accurate bird's-eye view (BEV) ground truth. With each scene lasting 16 seconds at a frame rate of 20 Hz, the SimBEV dataset contains 102,400 annotated frames, over 8 million 3D object bounding boxes, and more than 2.5 billion BEV ground truth labels.

SOTIF-PCOD is a dataset generated using the CARLA simulator, specifically designed for Safety of the Intended Functionality (SOTIF) research. It consists of 547 frames of LiDAR point cloud data formatted in the KITTI standard, representing a single SOTIF-related use case. The dataset captures a multi-lane highway scenario where an Ego-Vehicle, equipped with a LiDAR sensor, operates under varying environmental conditions. These conditions include different weather scenarios such as clear, cloudy, wet, and rainy environments, corresponding to different times of the day, including noon, sunset, and night.

Synthetic soccer players rendered on top of real world stadium images in 4K covering half a pitch each. Ground truth annotations in form of precise location of players on the pitch as well as 3D location of player pelvis and image bounding boxes.

10000 instances of three-view numerical data set with 4 clusters and 2 feature components are considered. The data points in each view are generated from a 2-component 2-variate Gaussian mixture model (GMM) where their mixing proportions $\alpha_1^{(1)}=\alpha_1^{(2)}=\alpha_1^{(3)}=\alpha_1^{(4)}=0.3$; $\alpha_2^{(1)}=\alpha_2^{(2)}=\alpha_2^{(3)}=\alpha_2^{(4)}=0.15$; $\alpha_3^{(1)}=\alpha_3^{(2)}=\alpha_3^{(3)}=\alpha_3^{(4)}=0.15$ and $\alpha_4^{(1)}=\alpha_4^{(2)}=\alpha_4^{(3)}=\alpha_4^{(4)}=0.4$. The means $\mu_{ik}^{(1)}$ for the first view are $[-10 ~-5)]$,$[-9 ~ 11]$, $[0~ 6]$ and $[4~0]$; The means $\mu_{ik}^{(2)}$ for the view 2 are $[-8 ~-12]$,$[-6 ~ -3]$, $[-2~ 7]$ and $[2~1]$; And the means $\mu_{ik}^{(3)}$ for the third view are $[-5 ~-10]$,$[-8 ~ -1]$, $[0~ 5]$ and $[5~-4]$. The covariance matrices for the three views are $\Sigma_1^{(1)}=\Sigma_1^{(2)}=\Sigma_1^{(3)}=\Sigma_1^{(4)}=\left[ \begin{array}{cc} 1 & 0\\0&1\end{array}\right]$; $\Sigma_2^{(1)}=\Sigma_2^{(2)}=\Sigma_2^{(3)}=\Sigma_2^{(4)}=3 \left[ \begin{array}{cc} 1 & 0\\0&1\end{array}\right]$; $\Sigma_3^{(1)}=\Sigma_3^{(2)}=\Sigma_3^{(3)}=\Sigma_3^{(4)}=2 \left[ \begin{array}{cc} 1 & 0\\0&1\end{array}\right]$; and $\Sigma_4^{(1)}=\Sigma_4^{(2)}=\Sigma_4^{(3)}=\Sigma_4^{(4)}=0.5 \left[ \begin{array}{cc} 1 & 0\\0&1\end{array}\right]$. These $x_1^{(1)}$ and $x_2^{(1)}$ are the coordinates for the view 1, $x_1^{(2)}$ and $x_2^{(2)}$ are the coordinates for the view 2, $x_1^{(3)}$ and $x_2^{(3)}$ are the coordinates for the view 3. While the original distribution of data points for cluster 1, cluster 2, cluster 3, and cluster 4 are 1514, 3046, 3903, and 1537, respectively.

The dataset consists of images of Human palms captured using a mobile phone. The images have been taken in a real-world scenario like holding objects or performing simple gestures. The dataset has a wide variety of variations like illumination, distances etc. It consists of images of 3 main gestures: Frontal-open palm, Back open palm and fist with the wrist. It also has a lot of images with people wearing gloves. Dataset Features: - Captured by 4000+ unique users - Covers wide variety of palm images in indoor and outdoor scenes - Images of palm with gloves - Captured by male and female - Distributed age groups like teenage, adults and old - Captured using mobile phones - Highly diverse - Various lighting conditions like day, night, indoor and outdoor - Outdoor scene with a variety of viewpoints Annotations Format: - Classification and detection annotations available - Multiple category annotations possible - COCO, PASCAL VOC and YOLO formats To download full datasets or to submit a request for your dataset needs, please ping us at (sales@datacluster.ai)[sales@datacluster.ai] Visit www.datacluster.ai to know more.

H3D (Humans in 3D) is a dataset of annotated people. The annotations include: * The joints and other keypoints (eyes, ears, nose, shoulders, elbows, wrists, hips, knees and ankles) * The 3D pose inferred from the keypoints. * Visibility boolean for each keypoint * Region annotations (upper clothes, lower clothes, dress, socks, shoes, hands, gloves, neck, face, hair, hat, sunglasses, bag, occluder) * Body type (male, female or child) Paper: Poselets: Body part detectors trained using 3D human pose annotations Source: H3D Image Source: H3D