# Hierarchical Guided-Image-Filtering for Efficient Stereo Matching
## 1 Introduction  
Stereo vision aims at providing rich distance information of the captured scenes via image pairs. This is normally accomplished by matching algorithms to generate dense disparity maps. The maps can be transformed into three-dimensional information of the scene by the principle of triangulation with many potential applications, such as autonomous navigation, 3D reconstruction, and vision-based object handling. Although the stereo matching problem has been under extensive research for decades, it is still difficult to obtain accurate matching under ill-posed conditions such as texture-less regions, repeated patterns, occlusion areas, and reflective surfaces. The current stereo matching algorithms can mainly be divided into two categories: The conventional matching algorithms [1] and the deep-learning-based stereo matching approaches. The stereo matching algorithm based on deep learning regards the process of deriving the disparity map as a classification problem or a regression problem. For instance, [PERSON] [2] used the convolutional neural networks (CNNs) to estimate the similarity of image blocks and uses the measures as the matching cost in the traditional stereo matching algorithm. Similarly, [PERSON] [3] proposed unsupervised pre-trained networks to estimate hierarchical features and combine them with a pixel-based intensity matching cost in a global energy minimization framework for densedisparity estimation. By combining a disparity estimation network with a CNN that was trained by a synthetically generated dataset, [PERSON] [4] demonstrated the effectiveness of deep learning in stereo matching. [PERSON] [5] proposed a cascaded CNN architecture that is composed of two stages: The first stage advances the work of [PERSON] [4] by equipping it with extra up-convolution modules, while the second stage generates residual signals across multiple scales. The summation of the outputs from the two stages gives the final disparity. [PERSON] [6] used deep unary features to compute a stereo matching cost volume. In this approach, disparity values are regressed for aggregation from the cost volume using 3D convolutions. Another method to implement deep learning-based stereo matching is to use the networks to exploit context information. For example, [PERSON] [7] developed a spatial pyramid pooling module to the aggregate context in different scales to form a cost volume. The cost volume is regularized by a stacked network to further improve the utilization of global context information. Besides, [PERSON] [8] used the deep learning technique for the cost volume aggregation based on self-guided filtering. Deep learning-based methods are promising as they can apply the high-level object detection as a guideline for within-object matching. However, most of the current schemes use supervised learning methods that assume the true disparity is known in advance. This assumption is impractical for many applications [9]. Moreover, these approaches might be invalid for an unknown environment and cannot be well transplanted to robotic and embedded systems [10]. The conventional stereo matching approaches are classified as global or local according to the construction of an objective function that rates the degree of match between an image pair [1]. The objective function of the global methods consists of a data term (the measurement part) and a regularization term (the penalty part). The data term designates the similarity between aggregated matching costs of pixels on the images, and the regularization term is included to provide constraints from neighboring pixels. Belief propagation [11] and dynamic programming [12; 13] are the major global methods. However, global approaches need a lot of computing resource and are generally not suitable for real-time applications. In contrast to the global approaches, the objective function of the local methods contains only the measurement part.