The ModelNet40 dataset contains synthetic object point clouds. As the most widely used benchmark for point cloud analysis, ModelNet40 is popular because of its various categories, clean shapes, well-constructed dataset, etc. The original ModelNet40 consists of 12,311 CAD-generated meshes in 40 categories (such as airplane, car, plant, lamp), of which 9,843 are used for training while the rest 2,468 are reserved for testing. The corresponding point cloud data points are uniformly sampled from the mesh surfaces, and then further preprocessed by moving to the origin and scaling into a unit sphere. Source: Geometric Feedback Network for Point Cloud Classification
The SHREC dataset contains 14 dynamic gestures performed by 28 participants (all participants are right handed) and captured by the Intel RealSense short range depth camera. Each gesture is performed between 1 and 10 times by each participant in two way: using one finger and the whole hand. Therefore, the dataset is composed by 2800 sequences captured. The depth image, with a resolution of 640x480, and the coordinates of 22 joints (both in the 2D depth image space and in the 3D world space) are saved for each frame of each sequence in the dataset. Source: Exploiting Recurrent Neural Networks and Leap Motion Controller for Sign Language and Semaphoric Gesture Recognition Image Source: http://tosca.cs.technion.ac.il/book/shrec.html
Washington RGB-D is a widely used testbed in the robotic community, consisting of 41,877 RGB-D images organized into 300 instances divided in 51 classes of common indoor objects (e.g. scissors, cereal box, keyboard etc). Each object instance was positioned on a turntable and captured from three different viewpoints while rotating. Source: Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work Image Source: https://rgbd-dataset.cs.washington.edu/
Cube++ is a novel dataset for the color constancy problem that continues on the Cube+ dataset. It includes 4890 images of different scenes under various conditions. For calculating the ground truth illumination, a calibration object with known surface colors was placed in every scene. Source: https://github.com/Visillect/CubePlusPlus Image Source: https://github.com/Visillect/CubePlusPlus
DOORS is a dataset designed for boulders recognition, centroid regression, segmentation, and navigation applications. The dataset is divided into two sets: - Regression: Contains images, masks, and labels for 4 splits of single boulders positioned on the surface of a spherical mesh. It can be used to perform navigation, boulder recognition, segmentation, and centroid regression. - Segmentation: Contain images, masks, and labels of 2 datasets: DS1 and DS2. DS1 is made of the same images of the Regression dataset but is specifically designed for segmentation. DS2 is made of images with multiple instances of boulders appearing on the surface of the Didymos asteroid model Source: DOORS: Dataset fOr bOuldeRs Segmentation. Statistical properties and Blender setup Image Source: https://arxiv.org/pdf/2210.16253v1.pdf
In this dataset an uppertorso humanoid robot with 7-DOF arm explored 100 different objects belonging to 20 different categories using 10 behaviors: Look, Crush, Grasp, Hold, Lift, Drop, Poke, Push, Shake and Tap.
The RBO dataset of articulated objects and interactions is a collection of 358 RGB-D video sequences (67:18 minutes) of humans manipulating 14 articulated objects under varying conditions (light, perspective, background, interaction). All sequences are annotated with ground truth of the poses of the rigid parts and the kinematic state of the articulated object (joint states) obtained with a motion capture system. We also provide complete kinematic models of these objects (kinematic structure and three-dimensional textured shape models). In 78 sequences the contact wrenches during the manipulation are also provided.
This dataset is the images of corn seeds considering the top and bottom view independently (two images for one corn seed: top and bottom). There are four classes of the corn seed (Broken-B, Discolored-D, Silkcut-S, and Pure-P) 17802 images are labeled by the experts at the AdTech Corp. and 26K images were unlabeled out of which 9k images were labeled using the Active Learning (BatchBALD) We have created three different dataset: (1). Primary dataset: contains the 17802 images labeled by the experts. Top-view(8901) and Bottom-view(8901). (2). Dataset with fake images: We generated fake images using Conditional GAN (BigGAN) as follows: broken-2937, discolored-5823, pure-2937, silkcut-5823 instances and added them into the train set to balance the data set. (3). Balanced dataset: In this case of adding newly captured images labeled using the Batch Active Learning method, new 9000 labeled images are added into the primary dataset. This new dataset contains 26,802 images split into train and validation set 80: 20, respectively. Contains the 17802 images and the 9K images labeled by the Active Learning (BatchBALD).