# 6D Pose Estimation of Objects: Recent Technologies and Challenges
## 3 Non-Learning-Based Approaches
### 2D-Information-Based Approaches
#### 3.1.1 CAD Image-Based Approaches  
In contrast to real image-based approaches, CAD image-based approaches are more suitable for industrial products. The virtual images (used as templates) generated by CAD models are more accurate than real images, as the render process is not affected by illumination or blur. Moreover, CAD models can be obtained in industrial applications. Therefore, many methods based on 3D CAD model have been proposed. A hierarchical model [53] has been proposed (Figure 6), combining a coarse-to-fine search with similarity scores [54] calculated between a template and a real image or between templates. In [55], a perspective cumulated orientation feature (PCOF) was proposed based on the orientation histograms extracted from randomly generated 2D projection images using CAD models. [PERSON] et al. [56] proposed the use of edge correspondences to estimate poses, with a similarity measure encoded using a pre-computed linear regression matrix. The Fine pose Parts-based Model (FPM) [57] was introduced to localize objects in an image, and to estimate their fine pose using the given CAD models. [PERSON] et al. [58] proposed a robust method that only used one pair of vanishing points and one structural line to estimate the relative pose between image pairs. [PERSON] et al. [59] proposed a method which used several cameras to detect geometrical features and then combined their results to obtain the final result.  
In [60], the Epipolar Geometry method and direct estimation method were used to estimate the 3D parameters, which were then used to construct the transformation matrix. In [61], 6D pose estimation was used in augmented reality. A local moving edge tracker was used to provide real-time tracking of points normal to the object contours. In addition, an M-estimator was used, integrated with a robust control law, to obtain good robustness. Straight lines and curves were both used in this method to complement the virtual visual servoing. In [62], 6D pose estimation was used for end effector tracking in a scanning electron microscope to aid in enabling more precise automated manipulations and measurements. Visible line features were also used to update the pose results. [PERSON] et al. [6] proposed a CAD model-based tracking method for visually guided microassembly. They used multiple cameras to track objects and find feature points along the edges of objects. Then, the 3D-3D for each feature point was built, and the 6D pose was calculated.  
Whether real images or CAD models are being used as the template, the performance of the matching step determines the accuracy of pose estimation. Improving the efficiency and accuracy of template matching has become an important problem to ensure the results of pose estimation.  
Additionally, the number of templates influences the accuracy of the pose estimation. The more templates there are, the more accurate the pose estimation will be. However, a large number of templates requires lots of storage and search time. In [63], a part-based efficient template matching method was proposed which was able to accelerate the matching step and improve the accuracy of pose estimation. Each of the templates leveraged a different forest and independently encoded similarity function.