This study presents six degrees of freedom (6-DoF) pose estimation of an object from a single RGB image and retrieval of the matching CAD model by measuring the similarity between RGB and CAD rendering images. The 6-DoF pose estimation of an RGB object is one of the important techniques in 3D computer vision. However, in addition to 6-DoF pose estimation, retrieval and alignment of the matching CAD model with the RGB object should be performed for various industrial applications such as eXtended Reality (XR), Augmented Reality (AR), robot’s pick and place, and so on. This paper addresses 6-DoF pose estimation and CAD model retrieval problems simultaneously and quantitatively analyzes how much the 6-DoF pose estimation affects the CAD model retrieval performance. This study consists of two main steps. The first step is 6-DoF pose estimation based on the PoseContrast network. We enhance the structure of PoseConstrast by adding variance uncertainty weight and feature attention modules. The second step is the retrieval of the matching CAD model by an image similarity measurement between the CAD rendering and the RGB object. In our experiments, we used 2000 RGB images collected from Google and Bing search engines and 100 CAD models from ShapeNetCore. The Pascal3D+ dataset is used to train the pose estimation network and DELF features are used for the similarity measurement. Comprehensive ablation studies about the proposed network show the quantitative performance analysis with respect to the baseline model. Experimental results show that the pose estimation performance has a positive correlation with the CAD retrieval performance.

Estimating the three-dimensional (3D) pose of real objects using only a single RGB image is an interesting and difficult topic. This study proposes a new pipeline to estimate and represent the pose of an object in an RGB image only with the 4-DoF annotation to a matching CAD model. The proposed method retrieves CAD candidates from the ShapeNet dataset and utilizes the pose-constrained 2D renderings of the candidates to find the best matching CAD model. The pose estimation pipeline consists of several steps of learned networks followed by image similarity measurements. First, from a single RGB image, the category and the object region are determined and segmented. Second, the 3-DoF rotational pose of the object is estimated by a learned pose-contrast network only using the segmented object region. Thus, 2D rendering images of CAD candidates are generated based on the rotational pose result. Finally, an image similarity measurement is performed to find the best matching CAD model and to determine the 1-DoF focal length of the camera to align the model with the object. Conventional pose estimation methods employ the 9-DoF pose parameters due to the unknown scale of both image object and CAD model. However, this study shows that only 4-DoF annotation parameters between real object and CAD model is enough to facilitates the projection of the CAD model to the RGB space for image-graphic applications such as Extended Reality. In the experiments, performance of the proposed method is analyzed by using ground truth and comparing with a triplet-loss learning method.

This paper proposes a 6-DoF pose estimation and CAD model retrieval for XR interface from a single RGB image. A deep learning network is used to estimate the 6-DoF pose of a real object in an RGB image. Then, several CAD model candidates are rendered and their similarity with the image is measured to decide the final matching CAD model. In the last, the rendered image of the matching CAD model is overlayed with the RGB image. In the experiment, total ten object categories are tested and evaluated using two deep networks.

This study addresses the challenge of accurate 6D pose estimation in Augmented Reality (AR), a critical component for seamlessly integrating virtual objects into real-world environments. Our research primarily addresses the difficulty of estimating 6D poses from uncontrolled RGB images, a common scenario in AR applications, which lacks metadata such as focal length. We propose a novel approach that strategically decomposes the estimation of z-axis translation and focal length, leveraging the neural-render and compare strategy inherent in the FocalPose architecture. This methodology not only streamlines the 6D pose estimation process but also significantly enhances the accuracy of 3D object overlaying in AR settings. Our experimental results demonstrate a marked improvement in 6D pose estimation accuracy, with promising applications in manufacturing and robotics. Here, the precise overlay of AR visualizations and the advancement of robotic vision systems stand to benefit substantially from our findings.