## 1 Introduction
Because each problem of 6-DoF pose estimation and CAD model retrieval from an input RGB is inherently not easy, simultaneous pose estimation and CAD model retrieval has not been commonly addressed. In this study, we propose a combined method of simultaneous 6-DoF pose estimation and CAD model retrieval from a single RGB image. From the single RGB image, we detect a real object and estimate the rotational and translational transformation of the object from the reference CAD to the camera coordinate systems. Then, the best-matching CAD model is retrieved from a dataset by the proposed similarity measurement between RGB and rendered CAD images. Our contributions are twofold, and the summary of the proposed methods is as follows. First, for 6-DoF pose estimation, we employ \"PoseContrast: Class-Agnostic Object Viewpoint Estimation in the Wild with the Pose-Aware Contrastive Learning\"(PoseContrast) [17] as the baseline model. PoseContrast estimates the 3-DoF rotational pose of an object regardless of the object classes. In the original PoseContrast, the number of public datasets for pose estimation is relatively small compared to those used for other tasks [18]. Therefore, using PoseContrast as the baseline model is advantageous since it is based on contrastive learning. In the proposed pose estimation network, we add new modules to the baseline to generate attention weights, which are used to update the feature output from the network rather than using features extracted by the multilayer perceptron (MLP) of the baseline network. Subsequently, several network modules are added to increase the attention weights of the important parts of the features. To do this, the probability variance of each 3-DoF rotational angle is estimated, and the attention weights to meaningful aspects of the features are refined. Then, the refined features are used as inputs for the MLP. Additionally, we enhance the network's performance by utilizing a loss function based on quaternion. Second, we propose a CAD model retrieval method to determine the best matching CAD model with the RGB object. The CAD model retrieval is based on deep features by \"Large-Scale Image Retrieval with Attentive Deep Local Features\" (DELF) [19], which has been introduced to find the 2D affine transformation between images. For the retrieval experiment, we select ten CAD candidates in each object class. Using the pose estimation result, all candidates are transformed to the camera coordinate system, and 2D projections are rendered. Then, image similarity between the candidates and the RGB object is measured to determine the best matching model. Therefore, the image similarity between the rendered CAD model and the RGB object is directly related to the pose estimation result. The better the pose of the RGB object is estimated, the more the image similarity between the matching CAD model and the RGB object is increased. To evaluate this correlation, we propose a correlation measurement between the two performances of pose estimation and CAD retrieval. The experimental results show that there is a positive correlation between the two performances. The architecture of the proposed method is shown in Figure 1 and it can be summarized as follows. First, an RGB object is detected and segmented from a single RGB image using DetectoRS [20]. Second, 3-DoF rotation transformation is estimated by the proposed pose estimation network. Third, by using Blender [21], several 2D rendered images of 3D CAD candidates of the detected object category are generated. Fourth, the best CAD model is retrieved by similarity matching between the rendered and the object images. Finally, the translation parameters of the RGB object are determined by aligning the CAD rendering with the RGB segmentation area. Figure 1: The architecture of the proposed 6-DoF pose estimation and CAD retrieval method. In the experiment, we show that the enhanced pose estimation performance also enhances the performance of CAD model retrieval.