## 1 Introduction
The disparity map and the depth map show good reconstruction accuracy indoors except for ambiguous regions like repetitive wall patterns where the intensity-based matching does not work. [PERSON] et al. [32] propose a dense matching algorithm based on interpolation of sparse points. The matchings points are processed during calibration, and results are stored in Look-Up Tables. The reconstruction algorithms are validated on synthetic images and real indoor images. [PERSON] et al. [4] present a catadioptric multi-stereo system composed of a single camera and five spherical mirrors. They implemented a dense stereo matching and multi point cloud fusion. The results presented in indoor scenes improved between 15% to 30% when combining the stereo results. Alternatively, deep learning techniques have recently obtained remarkable results in estimating depth from a single perspective image in indoor and outdoor environments. Depth estimation can be defined as a pixel-level regression, and the model architectures usually use Convolutional Neural Networks (CNNs) architectures. [PERSON] et al. [33] proposed predicting depth maps from RGB inputs in a multi-scale framework combining CNNs with Conditional Random Fields (CRF). Later in [34] they propose a multi-task architecture called PAD-Net capable of simultaneously perform depth estimation and scene parsing. [PERSON] et al. [35] introduced a depth estimation network that uses dilated convolutions and a full-image encoder to directly obtain a high-resolution depth map and improve the training time through depth discretization and the use of ordinal regression loss. More related to our work is the one from [PERSON] et al. [36], where an end-to-end deep learning model estimates depth from multiple fisheye cameras. Their network can learn global context information and reconstructs accurate omnidirectional depth estimates. However, all these methods require large training sets of RGB-depth pairs, which can be expensive to obtain, and the quality of the results is limited by the data used for training. Deep Learning techniques have also been applied in feature matching, and optical flow applications [37; 38]. [PERSON] et al. [39] proposed a graph neural network along with a context aggregation mechanism based on attention to allow their model to reason about the 3D information and the feature assignments. However, the performance is bounded by the training data used and the augmentations performed during training. To combine the best of both approaches (panoramic imaging and Deep Learning), in this work, we present a stereo catadioptric system that uses a deep learning-inspired feature matching algorithm called DeepMatching [40] augmented with stereo epipolar constraints. With this, our system can produce wide field-of-view 3D reconstructions in indoor and outdoor scenes without the need of specific training data. The main contributions of this work are twofold. First, we propose a catadioptric stereo system capable of generating semi-dense reconstructions using deep learning matching methods such as DeepMatching [40] with stereo epipolar constraints. Second, the system can produce 3D reconstructions in indoor or outdoor environments at higher framerate than dense methods without new training data. The rest of the paper is organized as follows: Section 2 illustrates the catadioptric vision system, Section 3 describes the methodology, Section 4 discusses the results, and Section 5 presents the conclusion.