Computer vision (CV)-based technologies have been used to automate construction progress monitoring. The automation attempts to maximise precision and minimise human intervention in onsite progress monitoring. Such attempts have mainly focussed on exterior construction environments while there are significantly lesser number of studies on interior construction. This imbalance impedes automation of the onsite progress monitoring as a whole. Thus, the core intent of this study is to pave the way for advancing automated indoor progress monitoring by providing a systematic survey of extant literature. Main contributions of this survey include 1) presenting a full spectrum of CV-based approaches, tools, and algorithms adopted for indoor construction progress monitoring (ICPM) 2) portraying a succinct reference to the shortcomings, technical challenges, and scope limitations of the past studies on ICPM. The study then synthesises a readily usable agenda for hybridising CV with other data-driven technologies to improve automation in ICPM.

基于计算机视觉（CV）的技术已用于自动化施工进度监控。自动化尝试在现场进度监控中最大程度地提高精度并减少人为干预。这样的尝试主要集中在外部建筑环境上，而对内部建筑的研究却明显较少。这种不平衡阻碍了整个现场进度监控的自动化。因此，本研究的核心目的是通过对现有文献进行系统的调查，为推进自动室内进度监控铺平道路。这项调查的主要贡献包括：1）介绍用于室内施工进度监控（ICPM）的各种基于CV的方法，工具和算法； 2）简要介绍缺点，技术挑战，以往关于植检临委的研究的范围和局限性。然后，该研究总结了一个易于使用的议程，用于将简历与其他数据驱动技术进行混合以改善ICPM的自动化。