# A Comprehensive Study on Deep Learning-Based 3D Hand Pose Estimation Methods
## 4 Datasets & Metrics
### Datasets  
In recent years, a few datasets suitable for 3D hand pose estimation have been proposed. Earlier datasets were composed only of depth data; however, since the development of robust methods that leverage RGB images, more datasets that contain both RGB and depth images have been introduced. In the following paragraphs, we have compiled and described the most frequently used datasets, omitting the ones that are rarely employed by the research community. Standard rules for dataset creation do not exist; therefore, the type of data (i.e., synthetic or real data), the number of joints and the resolution of the images varies between datasets. **Imperial College Vision Lab Hand Posture Dataset (ICVL)**[41] contains a collection of sequences from 10 different subjects, resulting in a total of 332.5 K depth images, 331 K for training and 1.5 K for testing, with 320 \(\times\) 240 resolution. The depth images have high quality and sharp outlines with little noise, but, as Reference [35] pointed out, a large number of samples are not correctly annotated. **NYU Hand Pose Dataset**[42] includes (RGB-D) frames from 3 Kinect cameras, providing a frontal and two side views. The training set consists of 72 K samples for training performed by one subject, while the test set contains 8.2 K samples from two subjects, with a resolution of 640 \(\times\) 480. Although it is composed of a wide variety of unique poses, its shortcoming is the fact that it provides only one hand shape in training set. In total, 36 joint are annotated but most works evaluate their performance on a subset of 14 joints. This dataset is considered challenging since depth maps exhibit typical artifacts of structured light sensors: noisy outlines and missing depth values. **BigHand2.2M**[139] is a large-scale real-world dataset that was recorded using six magnetic sensors attached on the hand, thus providing quite accurate 6D measurements. It consists of 2.2 M depth maps, obtained from 10 subjects with 640 \(\times\) 480 resolution. Locations of 21 joints were obtained by applying inverse kinematics on a hand model with 31 DOF with kinematic constraints. **MSRA15**[63] comprises about 76K RGB images with 21 annotated joints with 320 \(\times\) 240 resolution, captured using a time-of-flight camera. It contains 9 subjects performing 17 gestures covering a large number of viewpoints. For evaluation the most common schema is the leave-one-subject-out cross-validation; the training is conducted on 8 different subjects and the evaluation on the remaining one. The major drawbacks of this dataset is its limited size and the high error rate as far as annotations are concerned, e.g., missing finger annotations [139]. **Handnet**[138] includes 202 K training and 10 K testing depth images, as well as more than 2.7 K samples for validation, with a resolution of 320 \(\times\) 240. Samples were recorded with a RealSense RGB-D camera and depict the 6D postures of the hand, as well as the position and orientation of each fingertip. **HANDS 2017**[45] consists of about 1.1 M depth images at 640 \(\times\) 480 resolution, 957 K for training and 295K for testing, sampled from BigHand2.2M [139] and First-Person Hand Action [142], combining the large diversity of hand configurations, hand shapes, and viewpoints of Reference [139] and occlusions contained in Reference [142]. The training set consists of 5 subjects, while the test set