# Relative Pose Estimation between Image Object and ShapeNet CAD Model for Automatic 4-DoF Annotation
## 4 Experiments
### Performance Analysis of Similarity Measurement  
Tables 1-3 shows similarity matching results with three different measure criteria. As mentioned before, we use ten object categories and each category has 100 RGB test images, total 1000 RGB query images are used for experiments. However, at the object detection and pose estimation steps, some query images are rejected because of detection and estimation failure. Thus, in the similarity measurement experiment, total 596 query images are used.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **1-st Rank** & **2-nd Rank** & **3-rd Rank** & **Top-3** \\ \hline _Bag_ (19) & 7 & 2 & 0 & 9 \\ _Bed_ (24) & 4 & 7 & 3 & 14 \\ _Bench_ (63) & 38 & 10 & 1 & 49 \\ _Bottle_ (77) & 47 & 14 & 8 & 69 \\ _Car_ (80) & 33 & 11 & 8 & 52 \\ _Chair_ (80) & 45 & 21 & 3 & 69 \\ _Clock_ (59) & 27 & 12 & 4 & 43 \\ _Motorbike_ (76) & 37 & 8 & 8 & 53 \\ _Oven_ (79) & 34 & 10 & 10 & 54 \\ _Table_ (39) & 18 & 2 & 2 & 22 \\ \hline Total (596) & 290 & 97 & 47 & 434 \\ \hline Success rate (\%) & 48.65 & 16.27 & 7.88 & 72.81 \\ \hline \hline \end{tabular}
\end{table}
Table 1: CAD model retrieval results using the similarity measurement SM1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **1-st Rank** & **2-nd Rank** & **3-rd Rank** & **Top-3** \\ \hline _Bag_ (19) & 7 & 3 & 3 & 13 \\ _Bed_ (24) & 6 & 7 & 3 & 16 \\ _Bench_ (63) & 39 & 16 & 2 & 57 \\ _Bottle_ (77) & 41 & 15 & 14 & 70 \\ _Car_ (80) & 33 & 16 & 6 & 55 \\ _Chair_ (80) & 45 & 20 & 2 & 67 \\ _Clock_ (59) & 26 & 13 & 4 & 43 \\ _Motorbike_ (76) & 39 & 8 & 6 & 53 \\ _Oven_ (79) & 33 & 9 & 12 & 54 \\ _Table_ (39) & 20 & 4 & 1 & 25 \\ \hline Total (596) & 289 & 111 & 53 & 453 \\ \hline Success rate (\%) & 48.48 & 18.62 & 8.89 & 76.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: CAD model retrieval results using the similarity measurement SM2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **1-St Rank** & **2-Nd Rank** & **3-rd Rank** & **Top-3** \\ \hline _Bag_ (19) & 6 & 1 & 4 & 11 \\ _Bed_ (24) & 6 & 5 & 5 & 16 \\ _Bench_ (63) & 39 & 15 & 1 & 55 \\ _Bottle_ (77) & 41 & 14 & 15 & 70 \\ _Car_ (80) & 32 & 14 & 6 & 52 \\ _Chair_ (80) & 43 & 20 & 3 & 66 \\ _Clock_ (59) & 25 & 14 & 5 & 44 \\ _Motorbike_ (76) & 38 & 7 & 7 & 52 \\ _Oven_ (79) & 33 & 9 & 12 & 54 \\ _Table_ (39) & 16 & 6 & 3 & 25 \\ \hline Total (596) & 279 & 105 & 61 & 445 \\ \hline Success rate (\%) & 46.81 & 17.61 & 10.23 & 74.66 \\ \hline \hline \end{tabular}
\end{table}
Table 3: CAD model retrieval results using the similarity measurement SM3.  
In an ideal case, the similarity measurement finds the real matching CAD model which yields the highest similarity score. However, due to the texture quality of the CAD models, about 48% of the input finds the matching CAD models. Thus, we analyze the similarity measure up to the top-3 similar CAD images as shown in the tables. As shown in Tables 1-3, SM2 measurement shows the best performance, which simply counts the number of matching inliers. However, in terms of the top-first model retrieval, SM1 is the best measurement.