A structured review and taxonomy of next-best-view strategies for 3D reconstruction

Abstract

Next-Best-View (NBV) strategies are a class of approaches that solve the important problem of selecting the best possible viewpoints of an autonomous robot sensor for effective and complete 3D scene reconstruction. NBV methodologies have developed significantly over the years from rule-based approaches to those driven from deep learning. Consequently, NBV strategies have become diverse and uncategorized which makes it difficult for researchers and practitioners to navigate or standardize the methods. Therefore, in this paper, a comprehensive review was conducted to separate NBV methods into five distinct strategies: rule-based, uncertainty-based, sampling-based, learning-based, and prediction-based approaches. It is aimed to give a structured understanding after systematically reviewing over 100 publications including outlining key methodologies, open-access tools, and respective applications. Each strategy is investigated with related research questions such as understanding the role of geometric heuristics in rule-based methods, identifying efficient sampling mechanisms for exploration, leveraging predictive models for optimization, addressing uncertainty in unknown environments, and applying learning-based techniques to enhance adaptability and performance. Some suggestions are made for making classifications explicit, thus helping pull together more organized frameworks and collaborations across disciplines. This work not only offers a comprehensive resource for beginners and expert researchers but also empowers readers to answer strategy-specific research questions, providing actionable insights into NBV planning trends and emerging perspectives.