# 6D Pose Estimation of Objects: Recent Technologies and Challenges
## 4 Comparison  
In this section, we will compare these approaches in detail according to their performance in Table 1. Specifically, the accuracy, the storage cost, the robustness, the time cost, online performance and range of application will be discussed. The mentioned indicators are compared qualitatively in Table 1 according to the information in these papers, where A represents the best performance and C represents the worst performance. Accuracy: The accuracy of holistic approaches is worse than that of 2D-information-based approaches and 3D-information-based approaches. This is because holistic approaches transform the problem of 6D pose estimation into a problem of classification. The accuracy of classification decides the accuracy of 6D pose estimation. However, RGB-D-based approaches use more comprehensive information. The depth information is able to provide the overall morphology of a rigid body, while the color information is able to describe the position of keypoints. Therefore, RGB-D-based approaches that combine depth information and color information are able to improve the accuracy. 2D-information-based approaches and 3D-information-based approaches convert the 6D pose estimation problem into a template matching or coordinate transformation problem. Both of them are able to measure the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multirow{2}{*}{**Accuracy**} & \begin{tabular}{c} **Storage** \\ **Cost** \\ \end{tabular} & **Robustness** & **Time Cost** & \
\begin{tabular}{c} **Online** \\ **Performance** \\ \end{tabular} &
\begin{tabular}{c} **Range of** \\ **Application** \\ \end{tabular} \\ \hline \multirow{3}{*}{Learning-based approaches} & \multicolumn{1}{c}{Keypoint-based approaches} & B & B & B & C & C & B \\ \cline{2-6}  & \multicolumn{1}{c}{Holistic approaches} & C & B & B & B & B \\ \cline{2-6}  & \multicolumn{1}{c}{RGB-D-based approaches} & A & C & A & C & C & C \\ \hline \multirow{3}{*}{Non-learning-based approaches} & \multicolumn{1}{c}{2D-information-based approaches} & B & A & C & A & A & A \\ \cline{2-6}  & \multicolumn{1}{c}{3D-information-based approaches} & A & B & B & B & B & C \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of three kinds of approaches. 6D pose more specifically. 3D-information-based approaches use more information than 2D-information-based approaches, so they perform better on accuracy. Robustness: Robustness in this paper mainly refers to the anti-interference performance of approaches to noise and environmental changes. Plenty of data is used to train a network in learning-based approaches. The model adequately considers the information and situation in the input scene, so learning-based approaches have better robustness than the other two approaches. Additionally, using only color information may lead to missing keypoints, while depth information provides some global pose information and is complementary to color information. After a long period of training, learning models are able to distinguish object features and environmental noise. Although the other two approaches are also able to distinguish information from the environment efficiently, some wrong information may be taken into consideration under some complex situations where background clutter and foreground occlusion are present. Storage cost: Learning-based approaches need the most storage because the training process of the learning models needs plenty of data. The other two approaches need to store templates. However, the number of templates is lower than the training data required for learning-based approaches. In particular, for coarse-to-fine methods, which just need to match a basically similar template with the input image, much fewer templates are needed. In terms of a comparison between 2D-information-based approaches and 3D-information-based approaches, the former needs less storage, because 2D information is smaller than 3D information.