  UAV-based intelligent data acquisition for 3D reconstruction and monitoring
of infrastructure has experienced an increasing surge of interest due to 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.
