# A Comprehensive Study on Deep Learning-Based 3D Hand Pose Estimation Methods
## 2 Previous Categorizations and Early Machine Learning Methods  
### Previously Proposed Categorizations  
Various taxonomies have been proposed in order to categorize 3D hand pose estimation methods. [PERSON] et al. [20] considered two main categories: appearance-based and model-based approaches. Appearance-based methods [46; 47; 48; 49; 50; 51; 52] do not need a prior knowledge about the hand pose. Instead, they extract features from the input images and learn a direct mapping to the hand pose space, using either random forests or deep convolutional neural networks. The most distinguishing characteristic of the appearance-based methods is that they require extensive training, and they do not employ a hand model. Consequently, the performance of these algorithms depends heavily on the quality, as well as the quantity, of training data. Moreover, since the encoded image representations include all the necessary information that is needed to predict a hand pose, these methods are not computationally expensive. On the other hand, model-based algorithms [53; 54; 55; 56; 57; 58; 59] establish a prior 3D hand model to match the images into the predefined model. At each frame, an algorithm performs an exploration in order to acquire the pose and shape of the hand model that best matches the features extracted from the input image. These approaches require a good initialization for the first frame, while, for the subsequent ones, they can either utilize a prediction model or use the predicted pose from the previous frame. Afterwards a similarity function measures the discrepancy between the ground-truth pose and the selected one. Eventually, an optimization strategy is necessary for model convergence. The most popular optimization method is particle swarm optimization (PSO) [60] and iterative closest point (ICP) [61]. Model-based methods are better at handling self-occlusions and other factors, such as rotation and scaling, but this comes at a cost since they tend to be complex and computationally expensive. Furthermore, 3D hand pose estimation methods can be divided into regression-based [62; 63] and detection-based [43; 64] methods, according to the algorithm they apply. The key objective of methods that fall into the first category is to directly predict joint locations, which are continuous numeric values corresponding to the input image. In general, regression-based algorithms predict the output values based on input features extracted from training data, modeling dependencies between target poses and input images. On the contrary, detection-based methods predict a probability density map of each joint, where the probability at each point denotes how likely it is that the specific keypoint is located at this position. Specifically, these methods use volumetric heatmaps to form the joint location predictions and, afterwards, utilize an argmax function in order to obtain the position of a keypoint from a computed probability map. Another categorization can be defined based on the strategy of finger and palm pose estimation. Holistic methods [65; 66] estimate the hand pose by receiving the whole hand region as input, which is more robust but not very accurate at estimating fingers. Hierarchical methods [63; 67; 35] estimate the finger pose conditioned on the estimated palm pose. As a result, they achieve more accurate predictions, but they rely on the noisy estimation of palm pose. [PERSON] et al. [56] differentiated between disjoint evidence methods and joint evidence methods, according to the way that partial evidence regarding the individual rigid parts of the hand contributes to the final prediction. Disjoint evidence methods [68; 69] are computationally efficient, as they reduce the search space by considering individual parts in isolation, prior to evaluating them against observations. The disadvantage of these methods is that they need additional mechanisms to explicitly handle part interactions, such as collisions and occlusions.