# A Comprehensive Study on Deep Learning-Based 3D Hand Pose Estimation Methods
## 2 Previous Categorizations and Early Machine Learning Methods
### Early Machine Learning Methods  
Early works on the field of 3D hand pose estimation employ traditional machine learning techniques side by side with specialized technologies, such as gloves [73], that allow for better optimization. Amongst them, the Random Forest [74] algorithm and its variations are the most effective and excessively utilized. The Random Forest method is a machine learning ensemble meta-algorithm for classification or regression that falls under the category of bootstrap aggregating (bagging). It consists of a multitude of decision trees and outputs the class with the most predictions or the mean prediction of the individual trees, depending on the task. [PERSON] et al. [68], inspired by the huge success of Reference [69], applied the revolutionary--at the time--approach of intermediate representations on the hand pose estimation field, instead of directly inferring the hand pose in a high-dimensional space. Initially, a single depth image was segmented into a dense probabilistic hand part labeling. Subsequently, the authors employed a computationally efficient depth comparison and assigned at each pixel a hand part label with Random Forests, trained on a large amount of synthetic depth images. Lastly, the mean shift algorithm was employed to estimate the probability of each class label with weighted Gaussian kernels placed on each sample. The same authors proposed two novel multi-layered Random Forest networks, namely the Global Experts Network (GEN) and the Local Experts Network (LEN) [75], as an extension to their previous work. They trained Random Forests on clusters of the training set to classify each pixel into hand parts and introduced a Random Forest variation, called Shape Classification Tree (SCT) to assign a cluster label to each pixel. Then the input image was directed to the correct expert: either pose label for the whole image (GEN) or local clusters for individual pixels (LEN). However, since the above approaches required a large amount of per-pixel labeled samples, the use of synthetic depth images led to performance deterioration when applied in real-world data. [PERSON] et al. [76] addressed this drawback using a unified single-layered Semi-supervised Transductive Regression (STR) forest, which was trained on both a realistic and a synthetic dataset, in order to learn the underlying relationship between these sets. Yet, considering that the STR did not contain any structural information about the joints, a data-driven pseudo-kinematic technique was introduced to refine the joint predictions. [PERSON] et al. [77] proposed a hand parsing scheme to extract a high-level description of the hand from depth images, which was robust to complex hand configurations. They developed a depth-context feature and introduced a distance-adaptive selection method to further enhance its performance. Furthermore, they utilized a novel Superpixel Markov Random Field (SMRF) framework to model the spatial and temporal constraints, resulting in less misclassified regions, in comparison to pixel-level filtering methods. [PERSON] et al. [41] presented a novel Latent Regression Forest (LRF), a divide-and-conquer search method guided by a latent tree model that reflected the hierarchical structure of the hand. This structured coarse-to-fine search recursively divided the hand region until all joints were located, combining the benefits of both holistic and pixel-based methods, while the hand topological model enforced kinematic constraints. Methods that are based on pixel classification are less successful in the hand pose estimation field compared to human pose estimation. The major differences are the larger variations in finger articulation, occlusions, and camera viewpoints that are apparent in hand motions and the lack of structural information that these methods provide, leading to uncertain pixel classification, as [PERSON] et al. [63] pointed out.