# RSCS6D: Keypoint Extraction-Based 6D Pose Estimation
## 4 Experiments
### Comparison of Different Pruning Rates  
The lightweight pose estimation algorithm based on NS pruning with different pruning rates shows remarkable advantages on the LineMod dataset, as presented in Table 2. All pruned versions (20-50%) maintain a high accuracy of over 99.3% while keeping the processing speed at 36-37 FPS, surpassing the baseline method. Notably, the 30%-pruned version achieves an average accuracy of 99.5% at 37 FPS with only 10.81 MB of parameters, setting a new benchmark for studies on ten objects including ape and bench vise. Moreover, on symmetric objects eggbox and glue, the ADD-S metric remains at 100%.  
These results highlight the algorithm's significant improvements in accuracy, speed, and lightweight performance, with a considerable boost in overall effectiveness compared to traditional methods.  
The NS pruning strategy shows excellent model compression ability. Although time increases slightly, the total parameters decrease and the average ADD improves, showing NS pruning's strong model compression ability. The 30% pruned model offers the best balance, reducing parameters by 17.98% and raising ADD to 99.5% (+0.2%), while running at 37 FPS. The 50% pruned model, with 9.02 MB parameters (a 31.56% reduction), maintains 99.4% ADD, outperforming DenseFusion.  
Considering accuracy, speed, and model size, the 30% pruned model is optimal. It achieves 99.5% ADD, an 84.24% parameter reduction to 10.81 MB, and a 39.29% speed  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **PVNet [14]** &
\begin{tabular}{c} **PoseCNN +** \\ **DeepIm [42]** \\ \end{tabular} & **DenseFusion [20]** & **G2L-Net [27]** & **Ours** \\ \hline
**Input** & **RGB** & **RGB** & **RGB-D** & **RGB-D** & **RGB-D** \\ \hline Refinement & \(\times\) & \(\surd\) & \(\surd\) (\(\times\)) & \(\times\) & \(\times\) \\ ape & 43.6 & 77.0 & 92.3 (79.5) & 96.8 & **98.5** \\ bench vise & **99.9** & 97.5 & 93.2 (84.2) & 96.1 & 99.8 \\ camera & 86.9 & 93.5 & 94.4 (76.5) & **98.2** & **98.2** \\ can & 95.5 & 96.5 & 93.1 (86.6) & 98.0 & **99.3** \\ cat & 79.3 & 82.1 & 96.5 (88.8) & 99.2 & **99.8** \\ driller & 96.4 & 95.0 & 87.0 (77.7) & **99.8** & 99.5 \\ duck & 52.6 & 77.7 & 92.3 (76.3) & 97.7 & **98.3** \\ eggbox & 99.2 & 97.1 & 99.8 (99.9) & **100** & **100** \\ glue & 95.7 & 99.4 & **100** (99.4) & **100** & **100** \\ hole puncher & 81.9 & 52.8 & 92.1 (79.0) & 99.0 & **99.7** \\ icon & 98.9 & 98.3 & 97.0 (92.1) & 99.3 & 98.9 \\ lamp & 99.3 & 97.5 & 95.3 (92.3) & 99.5 & **99.7** \\ phone & 92.4 & 87.7 & 92.8 (88.0) & 98.9 & **99.4** \\ \hline mean & 86.3 & 88.6 & 94.3 (86.2) & 98.7 & **99.3** \\ \hline FPS & 30 & 6 & 19 (24) & 28 & **40** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of ADD(-S) metrics for similar algorithms on the LineMOD dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pruning Rate** & **Original** & **20\%** & **30\%** & **40\%** & **50\%** \\ \hline ape & **98.5** & **98.5** & **98.5** & **98.5** & 98.3 \\ bench vise & 99.8 & **100** & **100** & **100** & 99.8 \\ camera & 98.2 & 98.9 & **99.4** & 99.2 & 98.9 \\ can & 99.3 & 99.2 & 99.2 & 99.2 & 99.2 \\ cat & 99.8 & 99.8 & **99.9** & **99.9** & **99.9** \\ driller & 99.5 & 99.3 & 99.5 & 99.5 & 99.5 \\ duck & 98.3 & 98.4 & **98.6** & **98.6** & 98.3 \\ eggbox & **100** & **100** & **100** & **100** & **100** \\ glue & **100** & **100** & **100** & **100** \\ hole puncher & 99.7 & **99.8** & **99.8** & 99.7 & 99.6 \\ icon & 98.9 & 99.0 & 99.4 & 99.3 & 99.0 \\ lamp & 99.7 & 99.8 & **99.9** & 99.8 & 99.8 \\ phone & 99.4 & **99.5** & **99.5** & 99.4 & 99.4 \\ \hline mean & 99.3 & 99.4 & **99.5** & **99.5** & 99.4 \\ \hline FPS & **boost (37 FPS), making it the final RSCS6D_Pruned algorithm. In summary, combining point cloud keypoint extraction with NS pruning enhances pose estimation in accuracy, speed, and model volume, offering a practical solution for real-time pose estimation in resource-constrained scenarios.