## 7 Conclusions  
This paper tackles the problem of active robotic 3D reconstruction for novel objects using a single robot manipulator system. We proposed a NBV pose optimization method that efficiently identifies uncertain regions of the object using a gradient-based approach. Our pose optimization method, which directly moves to uncertain regions, offers a notable improvement in time efficiency compared to the fixed candidate approach that evaluates all pose candidates. To address the limited viewpoint issue of a single robot manipulator system, we introduced the See-Then-Grasp (STG) method, a two-stage robotic full 3D reconstruction pipeline. Initially, the robot learns the approximate geometry from reachable viewpoints, then grasps the object and expands the viewpoint by showing the uncertain \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Learning Rate/Optimization Step** & **PSNR**\(\uparrow\) & **LPIPS**\(\downarrow\) & **F-Score**\(\uparrow\) & **CD**\(\downarrow\) & **Time Cost (s)**\(\downarrow\) \\ \hline
0.1/10 & 23.13 & 0.0771 & 0.5185 & 0.2498 & 13.41 \\
0.1/20 & 23.19 & 0.0702 & 0.5548 & 0.1848 & 30.04 \\
0.1/40 & 23.42 & 0.0689 & 0.5741 & 0.1754 & 60.85 \\
1.0/10 & 25.49 & 0.0607 & 0.5713 & 0.1248 & 14.85 \\
1.0/20 (Ours) & 26.96 & 0.0564 & 0.6403 & 0.1071 & 29.69 \\
1.0/40 & 27.89 & 0.0479 & 0.6387 & 0.1046 & 60.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative results of the NBV pose optimizer with adjusted parameters for the Ficus dataset. We report the quantitative results from varying the learning rate and optimization steps of the NBV pose optimizer on the Ficus dataset. Our configuration achieves acceptable results with a reduced time cost compared to other settings. Higher values are better for PSNR and F-Score, while lower values are better for LPIPS, CD and time costs. Figure 10: Iterative 3D reconstruction results for the Toy car dataset. We present the qualitative results of the iterative 3D reconstruction on the Toy car dataset, showing that our method progressively captures both upper and lower views. region to an external fixed camera. We conducted experiments on both synthetic and real-world objects, demonstrating the effectiveness of our approach. Our results confirm that the proposed NBV pose optimization directs the robot to uncertain regions more quickly and accurately than other baseline methods, achieving a 60% improvement in computational efficiency. Furthermore, applying our method to real-world objects validates its capability to achieve full 3D reconstruction, including challenging occluded areas such as the bottom and back sides of objects. These findings underscore the potential of our method to significantly enhance the efficiency and quality of robotic 3D reconstruction in diverse environments. While this work successfully addresses several challenges in active robotic 3D reconstruction, it is limited by its reliance on a single manipulator and the inherent constraints of fixed-base systems. Future work will focus on extending this approach to multi-robot systems and exploring real-time 6D pose estimation [55, 56] to further improve reconstruction performance. By providing a robust framework for autonomous and complete object modeling, our method opens new opportunities for applications in manufacturing, logistics, and robotics research. Conceptualization, Y.H., J.K. and K.L. ; methodology, Y.H. and K.L. ; software, Y.H. and J.K.; validation, Y.H. ; writing--original draft preparation, Y.H., J.K. and K.L. ; writing--review and editing, Y.H., G.C., E.K. and K.L. ; supervision, G.C., E.K. and K.L. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2023-00211357) and the Chung-Ang University Graduate Research Scholarship in 2023. Not applicable. Informed Consent Statement: Informed consent was obtained from all the subjects involved in this study.