## 1 Introduction  
In 3D computer vision, 6-DoF (degrees of freedom) pose estimation is a study of finding the 3D position and 3D orientation of a real object in images or videos. Precise knowledge of the object's 6-DoF pose with respect to the camera coordinate system enhances the performance in various applications such as eXtended Reality (XR), Augmented Reality (AR), robotic pick and place, autonomous vehicle, etc. In the studies of robotic pick and place, precise pose estimation is essential for picking up objects using camera images. The complete pose information of an object can be effectively used even when the object is partially occluded. In the studies of autonomous vehicles, estimating the pose of surrounding objects is necessary for recognizing the environment and obstacles, thereby enabling stable and safe driving [1]. For XR and AR applications, 6-DoF pose estimation of real objects is necessary to precisely align graphic models with the objects in the image space. A graphic model, such as a CAD model in the world coordinate system, should be transformed and projected to the image plane of the camera so that the projection of the CAD model is precisely aligned with the object area. Especially for XR and AR applications, the shape-matching model with the real object should be retrieved from a CAD model database before aligning the CAD model with the object in the image space. Therefore, not only the pose estimation of an RGB object but also the CAD model retrieval problem should be addressed simultaneously to align the RGB real object and CAD model exactly. Conventional computer vision methods for estimating 6-DoF pose involve extracting and matching image features. Well-known image features include Scale-Invariant Feature Transform (SIFT) [2], Speeded Up Robust Features (SURF) [3], Oriented FAST and Rotated BRIEF (ORB) [4], and so on. Conventional feature-based 6-DoF pose estimation methods face limitations when the number of extracted features is small or when an object is placed with a complex background due to high sensitivity to data quality and outliers. Other 6-DoF methods include Iterative Closest Point (ICP) [5] and Perspective-n-Point (PnP) algorithm [6]. ICP estimates correspondences between different point clouds to find rotation and translation transformations, and PnP estimates object pose using the perspective projections between 3D points and corresponding 2D image points. Recent advances in deep learning techniques and the availability of big data have overcome some limitations of traditional feature-based methods. In addition to RGB images, 3D data from depth cameras, such as RGB-D and point clouds, have been utilized. Several approaches have been developed to estimate the 6-DoF pose of objects using deep learning methods. PoseCNN [7] and Deep-6 DPose [8] employ convolutional neural networks (CNNs) for regression-based pose estimation. PoseNet [9] and PPR-Net [10] use point clouds as input data for pose estimation, while DenseFusion [11] combines various data types to extract comprehensive information for accurate pose estimation. Furthermore, methods such as Dpod [12] and Rnnpose [13] utilize deep learning networks to predict object pose even when parts of the object are occluded. These advancements highlight the capability of deep learning networks to estimate object pose more accurately, even in the presence of various obstacles. The 3D CAD model retrieval problem is to search for a CAD model from CAD datasets, which is the best matching with an input RGB object. Conventional CAD retrieval methods mostly address the search for matching CAD models from RGB or RGB-D images [14]. However, there are few studies of simultaneous searching for the best CAD model and estimating the 6-DoF pose of an input RGB image [15; 16]. [PERSON] et al. [15] and [PERSON] et al. [16] introduced frameworks similar to the proposed method. These methods detect an RGB object from an input image, estimate the 4-DoF or 6-DoF pose, and search the matching CAD model.