UAV-based intelligent data acquisition for 3D reconstruction and monitoring of infrastructure has been experiencing an increasing surge of interest due to the recent advancements in image processing and deep learning-based techniques. View planning is an essential part of this task that dictates the information capture strategy and heavily impacts the quality of the 3D model generated from the captured data. Recent methods have used prior knowledge or partial reconstruction of the target to accomplish view planning for active reconstruction; the former approach poses a challenge for complex or newly identified targets while the latter is computationally expensive. In this work, we present Bag-of-Views (BoV), a fully appearance-based model used to assign utility to the captured views for both offline dataset refinement and online next-best-view (NBV) planning applications targeting the task of 3D reconstruction. With this contribution, we also developed the View Planning Toolbox (VPT), a lightweight package for training and testing machine learning-based view planning frameworks, custom view dataset generation of arbitrary 3D scenes, and 3D reconstruction. Through experiments which pair a BoV-based reinforcement learning model with VPT, we demonstrate the efficacy of our model in reducing the number of required views for high-quality reconstructions in dataset refinement and NBV planning.

翻译：基于无人机的智能数据采集在基础设施三维重建与监测领域正受到日益广泛的关注，这得益于图像处理与深度学习技术的进步。视角规划是该任务的核心环节，它决定了信息采集策略，并深刻影响着所采集数据生成的三维模型质量。现有方法或依赖先验知识，或利用目标的部分重建结果来实现主动重建的视角规划：前者对复杂或新识别目标构成挑战，后者则计算成本高昂。本研究提出"视角集"（Bag-of-Views, BoV）——一种全外观模型，用于为已采集视角分配效用值，可应用于离线数据集优化与面向三维重建的在线下一最佳视角（NBV）规划。基于此贡献，我们还开发了视角规划工具箱（View Planning Toolbox, VPT），这是一个轻量级工具包，支持基于机器学习的视角规划框架的训练与测试、任意三维场景的自定义视角数据集生成以及三维重建。通过将基于BoV的强化学习模型与VPT结合进行实验，我们证明了该模型在数据集优化与NBV规划中，能有效减少高质量重建所需的视角数量。