###### Abstract
The main advantage of such methods is their ability to handle the occlusion problem by identifying parts of an object in cluttered scenes. The target of these studies is mainly the robotic manipulation of specific objects, such as chairs and boxes, and human-robot interaction. Deep neural networks have also been used for 6-DoF tracking of rigid objects in large occluded environments [16; 19]. However, because these methods require red, green, blue, and depth (RGB-D) sensing data, their practical use is constrained to indoor scenes. Although camera pose estimation has been more precise with recent structure-based methods than with learning-based models, their performance is affected by the limitations of the feature tracking techniques. For instance, SIFT-based SfM methods require that the temporal interval between the two images is very short for feature tracking to work successfully [9]. In the case of a small baseline, most regions of the scene overlap in consecutive images. However, feature tracking fails if the baseline is too high, as the regions in consecutive images no longer overlap. Since deep learning-based models can learn scene features, they estimate the camera pose directly from a single image, with no need for feature tracking. Thus, many end-to-end deep learning methods have near real-time inference times and an appealing simple pipeline. In contrast, SfM methods need large-scaled prior information about the scene to estimate the camera pose from an input image. This means that SfM methods need higher computational and memory costs than deep learning models. However, the localization errors obtained by deep learning models have generally been larger than with structure-based methods. Coarse-to-fine localization using learned image-wide global (for image retrieval) and local descriptors (for 2D-3D matching) has been introduced to improve the localization performance of deep learning-based methods [14; 15]. Here, the retrieval process is used to obtain the k-closest images to a given image, for localization at the map level (location hypotheses). A precise pose is subsequently estimated based on 2D-3D matches within this candidate space. While SIFT was used as a local descriptor in [14], with subsequent matches made using a k-d tree, it generates a large number of features, making 2D-3D matching particularly computationally expensive, with real-time local matching based on SIFT being similarly expensive. To address this, the hierarchical feature network (HF-Net) detects interest points and computes local and global descriptors collectively, maximizing the sharing of computations [15]. This model detects and matches interest points, which are reconstructed into sparse 3D models with SfM techniques, using SuperPoint [20]. However, studies of hierarchical models, such as those detailed above, do not present any scheme for constructing a training dataset for 2D-3D matching, and in such models, the limitations of the SfM technique remain unsolved. Many deep learning methods have been proposed for establishing a pixel-level correspondence between images [20; 21; 22; 23]. The approach adopted by the SuperPoint architecture is to reduce the dimensionality of the image to be processed with a Visual Geometry Group at University of Oxford (VGG)-style [24] encoder. Subsequently, regression tasks are divided by splitting the architecture into two decoder heads, for pixel-level interest point detection and for interest point description [20]. SuperPoint requires point pre-training from a synthetic dataset consisting of simple geometric shapes with no ambiguity in the interest point locations. Additionally, to help the point detector recognize the scene from several different viewpoints and scales, homographic adaptation is applied to warp the input image. Since changes in homography do not sufficiently reflect possible visual changes in scenes that have several complicated surfaces, [PERSON] et al.