## 5 Experiment
### Experiment on Synthetic Objects
#### 5.1.4 Results  
The quantitative results of our experiments are presented in Table 1, while the qualitative results of our baseline are shown in Figure 5. According to Table 1, our baseline outperforms other baselines in terms of rendering and geometric quality when 24 NBV images are added. This performance difference is particularly significant for geometrically complex datasets, such as Lego and Ficus. Even with the addition of 48 NBV images, our baseline demonstrates superior results, although the other baselines tend to converge to a similar level.  
To further evaluate the NBV selection performance of each baseline, we present the qualitative comparison results in Figure 6 and the F-score progression for the Ficus dataset in Figure 7. These results indicate that our baseline reaches the oracle line more rapidly than other methods and effectively captures both the geometric details and the overall shape.  
Figure 5: Qualitative 3D reconstruction results of our baseline. We present the 3D reconstruction of four synthetic objects, improved by adding 48 NBV images to our baseline. The ground truth images are on the left, with the 3D reconstruction meshes on the right, showcasing our method’s ability to accurately capture fine details.  
Finally, the time cost measurements for each baseline are illustrated in Figure 8. Our NBV pose optimization efficiently identifies uncertain regions without exploring all view  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Lego**} & \multicolumn{4}{c}{**Ficos**} & \multicolumn{4}{c}{**Chair**} & \multicolumn{4}{c}{**Mic**} \\ \hline
**Method** & **PSNR \(\uparrow\)** & **LPIPS \(\downarrow\)** & **F-Score \(\uparrow\)** & **CD \(\downarrow\)** & **PSNR \(\uparrow\)** & **LPIPS \(\downarrow\)** & **F-Score \(\uparrow\)** & **CD \(\downarrow\)** & **PSNR \(\uparrow\)** & **LPIPS \(\downarrow\)** & **F-Score \(\uparrow\)** & **CD \(\downarrow\)** & **PSNR \(\uparrow\)** & **LPIPS \(\downarrow\)** & **F-Score \(\uparrow\)** & **CD \(\downarrow\)** \\ \hline \multicolumn{10}{c}{Quality after 24 NBV images added} \\ \hline Grad + Entropy (Ours) & 31.69 & 0.0508 & 0.4022 & 0.2613 & 26.96 & 0.0564 & 0.6403 & 0.1071 & 31.30 & 0.0319 & 0.3810 & 0.3345 & 30.26 & 0.0312 & 0.4010 & 0.3045 \\ Grad + Ensemble [42] & 30.48 & 0.067 & 0.3908 & 0.2784 & 24.0780 & 0.6129 & 1.479 & 31.46 & 0.0312 & 0.3891 & 0.3168 & 25.85 & 0.0319 & 0.4901 & 0.2688 \\ Fixed [17,18] + Entropy & 29.29 & 0.0862 & 0.3651 & 0.3605 & 25.41 & 0.0544 & 0.5308 & 1.0899 & 0.313 & 0.0394 & 0.3887 & 0.3214 & 29.11 & 0.0394 & 0.4098 & 0.2848 \\ Fixed [17,18] + Ensemble [42] & 30.26 & 0.0522 & 0.3819 & 0.6322 & 24.11 & 0.0798 & 0.6184 & 0.1381 & 31.85 & 0.0329 & 0.3898 & 0.3148 & 27.73 & 0.0389 & 0.4087 & 0.2914 \\ FVS & 28.63 & 0.0611 & 0.3509 & 0.4427 & 24.52 & 0.0539 & 0.5003 & 0.2215 & 29.97 & 0.0464 & 0.3688 & 0.4269 & 26.15 & 0.0464 & 0.3888 & 0.4169 \\ Random & 27.46 & 0.0789 & 0.2649 & 0.7026 & 23.26 & 0.0671 & 0.4834 & 0.3083 & 28.56 & 0.0641 & 0.3464 & 0.5908 & 26.30 & 0.0641 & 0.3664 & 0.5938 \\ \hline \multicolumn{10}{c}{Quality after 48 NBV images added} \\ \hline Grad + Entropy (Ours) & 34.73 & 0.0319 & 0.4886 & 0.1408 & 34.94 & 0.0204 & 0.8428 & 0.0504 & 35.86 & 0.0275 & 0.4168 & 0.2844 & 34.15 & 0.0230 & 0.7241 & 0.0785 \\ Grad + Ensemble [42] & 34.04 & 0.0321 & 0.4711 & 0.1425 & 34.13 & 0.0244 & 0.8149 & 0.1048 & 36.10 & 0.0248 & 0.4216 & 0.2805 & 34.79 & 0.0202 & 0.7211 & 0.0736 \\ Fixed [17,18] + Entropy & 34.16 & 0.0324 & 0.4295 & 0.2857 & 34.51 & 0.0203 &candidates through a single NBV pose optimization. As a result, our method solves the NBV selection problem 60% faster compared to the fixed view candidate method.