# Semantic 3D Reconstruction for Robotic Manipulators with an Eye-In-Hand Vision System
## 4 Experimental Results
### Three-Dimensional Reconstruction Results
(**a**) The original image, (**b**) the reconstruction results of the mask-only method, (**c**) the reconstruction results of the mask + ICP method, (**d**) the reconstruction results of the SLIC + ICP method, and (**e**) the reconstruction results of our method. Figure 5: Our dataset results on 3D reconstruction IoU (%). Our method significantly improved the 3D reconstruction IoU compared with the mask-only method, the mask + ICP method, and the SLIC + ICP method by two-way Analysis of Variance (ANOVA) repeat measures with [PERSON]’s multiple comparison test (\(\textbf{{}^{rec}}\) \(p\) < 0.001). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Cylinder** & **Half-Cylinder** & **L-Block** & **Cuboid** & **Box** \\ \hline Mask-only [40] & 4.786 & 5.851 & 5.622 & 4.806 & 3.442 \\ Mask + ICP [34] & 3.534 & 4.597 & 4.535 & 3.250 & 3.083 \\ SLIC + ICP [44] & 3.504 & 4.250 & 4.380 & 3.262 & 3.012 \\ Our method & 3.449 & 4.074 & 4.142 & 2.992 & 2.973 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Cloud to Cloud (C2C) absolute distances between our dataset models and 3D reconstruction (mm). Similarly, we validate our algorithm on the YCB Benchmarks. The C2C absolute distances between YCB models and 3D reconstruction by four methods are shown in Table 4. The C2C absolute distance can be used to evaluate the similarity between 3D reconstruction results and object models. The lower the value, the higher the accuracy of 3D reconstruction and the more significant the similarity is between the object models. The results in Table 4 indicate that the C2C absolute distances of each object decrease successively in the four methods of mask-only, mask + ICP, SLIC + ICP, and our method, which suggests that the 3D reconstruction results of our method are closer to the object models with highest accuracy. Compared with the other three methods, our method improves the accuracy of image segmentation and reduces the number of outlier points, so the 3D reconstruction results of our method is more accurate. The YCB dataset results on 3D reconstruction are shown in Figure 6. Since the point cloud model of the YCB Benchmarks is obtained by the depth camera in multi-angle shooting, it is closer to the actual situation than the point cloud model generated by Computer Aided Design (CAD), so the four methods perform better on the YCB Benchmarks. As shown in Figure 6, the average accuracy of our method on the YCB Benchmarks is 13.65% over the mask-only method, 4.01% over the mask + ICP method, and 3.27% over the SLIC + ICP method. We counted the average CPU time of each methods, as shown in Table 5. Due to the missing geometric feature segmentation and camera pose optimization, the mask-only method ran fastest with lowest precision. Without geometric feature segmentation, the mask + ICP method saved time in segmentation, but the precision of the 3D reconstruction was still poor. The SLIC + ICP method balanced performance and CPU time. Our method took a little more time in segmentation than the SLIC + ICP method, but we saved much more time in the 3D mapping. Because we utilized the SICP method to remove unrelated objects and accelerate point clouds matching. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Lemon** & **Pear** & **Orange** & **Plum** & **Bowl** & **Mug** \\ \hline Mask-only [40] & 3.727 & 8.717 & 4.802 & 4.918 & 4.079 & 3.601 \\ Mask + ICP [34] & 3.646 & 6.244 & 4.240 & 4.574 & 3.341 & 3.131 \\ SLIC + ICP [44] & 3.121 & 5.849 & 4.106 & 4.421 & 3.251 & 3.110 \\ Our method & 2.593 & 5.406 & 3.586 & 3.693 & 3.067 & 2.968 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: C2C absolute distances between YCB models and 3D reconstruction (mm). Figure 6: YCB dataset results on 3D reconstruction IoU (%). Our method significantly improved the 3D reconstruction IoU compared with the mask-only method, the mask + ICP method, and the SLIC + ICP method by two-way ANOVA repeat measures with [PERSON]’s multiple comparison test (*** \(p\) < 0.001).