This scientific poster, entitled "GenNBV: Generalizable Next-Best-View Policy for Active 3D Reconstruction," presents a comprehensive study on optimizing the process of 3D reconstruction using advanced methodologies. The authors, Xiao Chu, Quanyi Lu, Ti Wang, Tianfu Xu, and Jiangmiao Pang, from OpenRetrieve, Shanghai AI Laboratory, and the Chinese University of Hong Kong, elaborate on various facets of their research.

**Motivation:**
The poster delves into the motivation behind the study, emphasizing the need for efficient 3D positioning in high-precision applications such as augmented reality and robotics. It highlights the limitations of existing methodologies in terms of computing efficiency and adaptability.

**Methods:**
The methodology section outlines the framework employed in constructing an efficient 3D reconstruction system. It includes the use of simulation data for training policies that predict the 'Next-Best-View' (NBV) and includes a pipeline for both object and scene level 3D reconstructions.

**Visualizations:**
Visual results of the approach demonstrate significant improvement in reconstruction quality. The poster provides examples of 3D reconstructions obtained from the proposed method, showcasing its effectiveness compared to traditional methods.

**Contributions:**
The key contributions are summarized, illustrating the creation of a generalizable framework that markedly improves NBV policies for unseen objects and unique scene datasets. Multiple graphs and tables display quantitative results that underline the superior performance of GenNBV.

**Additional Studies:**
Additional studies and supplementary analyses are included to further validate the robustness and scalability of the proposed method. 

**Contact and Resources:**
For further detail, contact information and a QR code are provided, potentially linking to a repository of code or further documentation on the study.

This poster was likely presented at a scientific conference or symposium, providing novel insights and proposing a significant advancement in the field of active 3D reconstruction.
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Motivation
Exing Nity
GenNBV: Generalizable Next-Best-View Policy for Active 3D Reconstruction
Xiao Chan Quany L Tal Wang Tentan Xue Jangnias Pang
Opetab Shangha Labory
The Orem Usersity of Hong Kong
Methodology
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+ Project Page AR
+ Contact GRANAMAN
Yer Capturing
Kay s
ability Evaluates
Ablation Stody