###### Abstract
included a correspondence contrastive loss function in a fully convolutional architecture, and a convolutional spatial transformer to mimic SIFT's patch normalization [22]. Similarly, with D2-Net, detection and description parameters are shared, with a joint formulation used for both tasks [21]. Hence, to train deep learning models to identify detected interest points based on a description, the training dataset should include a large number of image pairs and correspondence point sets, as well as consider effects such as illumination and viewpoint changes. The goal of our study is to estimate a 6-DoF camera's pose from an input image related to a learned representation of a known scene. To train the deep learning-based camera pose estimator and evaluate its performance, we require image sets consisting of scenes and the ground truth poses of the cameras that captured them. Since the camera pose is related to the coordinates of a 3D model, a dataset (typically a 3D point cloud) that considers this, such as Kendall's outdoor urban localization dataset, Cambridge Landmarks [3], is also necessary. These datasets included input images and 3D points reconstructed using the SfM technique [2] and correspondence sets over images to be used as training and test data. This dataset can be downloaded at the following URL ([[http://mi.eng.cam.ac.uk/projects/relocalisation/](http://mi.eng.cam.ac.uk/projects/relocalisation/)]([http://mi.eng.cam.ac.uk/projects/relocalisation/](http://mi.eng.cam.ac.uk/projects/relocalisation/))). The log file informs us which image sequence is the training or test dataset. Our training dataset consists of 3D points and image pairs with point-based correspondence. Here, although some image pairs are from consecutive frames, we have also included image pairs with a large baseline, to ensure the effects of changes in camera viewpoint and illumination can be considered in our training procedure. In this study, scenes were digitized using identical voxels, consisting of 3D points in the scene space, divided according to their global location. The main plane, i.e., the plane with the most 3D points in each voxel (typically an exterior plane like a wall or the ground in the scene), is extracted using random sample consensus (RANSAC)-based plane fitting. Then, two networks are trained; the segmentation model, to learn the features of inlier 3D points in the voxel and then, detect the voxel regions in a query image, and the SuperPoint model [20], to extract the location and description of the interest points from the image, which correspond to the inlier 3D points on the main plane. Additionally, since the interest points are distributed on the main plane of the voxel, their correspondence set across images satisfies the homography relation. Therefore, the voxel-based scene representation and training dataset architecture proposed is useful in pixel-level correspondence studies techniques on deep learning [21; 22]. A flow chart of the proposed system is depicted in Figure 1. Here, 2D-3D Matching is performed by searching through the learned descriptor space. In our scene representation, each voxel is labeled with a unique identification number. We examine whether the voxel regions detected in an input image maintain geometric consistency (such as left-right order). This process enables us to remove false matches among 2D-3D correspondences. Given accurate 2D-3D matches, we employ an n-point pose (PnP) solver in the RANSAC loop to estimate 6-DoF camera poses [25]. The rest of the paper is organized as follows. In Section 2, we describe the scene representation, feature detection, and camera pose estimation processes employed in our voxel-based technique. The experimental setup used in this study is detailed in Section 3. Then, we discuss the results of our experiments in Section 4 and conclude the paper in Section 5.