Implicit neural representations have demonstrated significant promise for 3D
scene reconstruction. Recent works have extended their applications to
autonomous implicit reconstruction through the Next Best View (NBV) based
method. However, the NBV method cannot guarantee complete scene coverage and
often necessitates extensive viewpoint sampling, particularly in complex
scenes. In the paper, we propose to 1) incorporate frontier-based exploration
tasks for global coverage with implicit surface uncertainty-based
reconstruction tasks to achieve high-quality reconstruction. and 2) introduce a
method to achieve implicit surface uncertainty using color uncertainty, which
reduces the time needed for view selection. Further with these two tasks, we
propose an adaptive strategy for switching modes in view path planning, to
reduce time and maintain superior reconstruction quality. Our method exhibits
the highest reconstruction quality among all planning methods and superior
planning efficiency in methods involving reconstruction tasks. We deploy our
method on a UAV and the results show that our method can plan multi-task views
and reconstruct a scene with high quality.

通过引入基于前沿探索任务的全局覆盖和基于隐式表面不确定性的重建任务，结合使用颜色不确定性实现隐式表面不确定性的方法，提出了一种自适应策略来在视角路径规划中切换模式，以减少时间并保持卓越的重建质量。该方法在所有规划方法中展示了最高的重建质量和在涉及重建任务的方法中的优越规划效率。我们在一架无人机上部署了我们的方法，并且结果显示我们的方法可以计划多任务视图并以高质量重建场景。

自主隐式室内场景重建与边界探索

Autonomous Implicit Indoor Scene Reconstruction with Frontier  Exploration

We present a classification based approach for the next best view selection
and show how we can plausibly obtain a supervisory signal for this task. The
proposed approach is end-to-end trainable and aims to get the best possible 3D
reconstruction quality with a pair of passively acquired 2D views. The proposed
model consists of two stages: a classifier and a reconstructor network trained
jointly via the indirect 3D supervision from ground truth voxels. While
testing, the proposed method assumes no prior knowledge of the underlying 3D
shape for selecting the next best view. We demonstrate the proposed method's
effectiveness via detailed experiments on synthetic and real images and show
how it provides improved reconstruction quality than the existing state of the
art 3D reconstruction and the next best view prediction techniques.

通过分类的方法，提出了一种下一个最佳视角选择的方案，着重于通过一对被动获取的 2D 视图获得最佳可能的 3D 重建质量，并通过地面真实体素的间接 3D 监督来联合训练分类器和重构网络。