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：基于无人机的基础设施三维重建与监测智能数据采集技术，随着图像处理与深度学习技术的进展正受到日益广泛的关注。视角规划作为该任务的核心环节，直接决定信息采集策略并显著影响从采集数据生成的三维模型质量。现有方法通过目标先验知识或局部重建实现主动重建的视角规划，前者对复杂或新型目标具有挑战性，后者则计算成本过高。本文提出全外观驱动的视图包模型，通过为已采集视图分配效用值，可同时应用于离线数据集优化与面向三维重建的在线最佳下一视角规划任务。基于该贡献，我们开发了视角规划工具箱——一个用于训练测试基于机器学习的视角规划框架、生成任意三维场景定制视图数据集及实现三维重建的轻量级工具包。通过将基于BoV的强化学习模型与VPT结合的实验，验证了本模型在数据集优化与NBV规划中减少高质量重建所需视图数量的有效性。