Video based Progress Monitoring of Finishing Works based on 4D Building Information Models

完工工程的实施通常会偏离计划的进度。一方面，这是由于固有的设计不确定性，另一方面，由于不可避免的中断和设计更改。因此，持续监测进展情况对于立即采取适当行动至关重要。目前，进度监控是一个非常耗费人力、耗时和主观的过程，因为现场数据是手动捕获的，并与项目信息保持一致。因此，对进度的偏离往往被发现得太晚，从而导致广泛且代价高昂的反制和设计更改。因此，稳健、高效的施工需要实时、透明和准确的进度监控。在本研究项目中，开发了基于视频竣工数据和4D竣工信息模型(BIM)的自动化进度监控的创新和基本方法。这包括，例如，用于基于模型的相机位置和方向估计的方法，以便在4D BIM内登记视频。因此，可以将建筑设计信息直接映射到捕获的图像数据上。在此基础上，设计了机器视觉和学习方法来检测和识别相关的完工项目及其完成状态。为此，定义进度和观看方向相关的对象和状态特征，并将其存储在可扩展对象目录中。此外，基于4D BIM的存储和链接相关位置、几何和材料分类器的新概念以及它们的持续适应性是本研究的重点。预计结果将显著提高精修工程进度监测的准确性、稳健性和效率。

The execution of finishing works is usually characterized by deviations from the planned schedule. On the one hand, this is due to the inherent design uncertainties and, on the other hand, due to inevitable disruptions and design changes. For this reason, continuous progress monitoring is crucial to instantly take appropriate actions. Currently, progress monitoring is a very labor intensive, time consuming and subjective process, since on-site data is manually captured and aligned with project information. Accordingly, deviations from schedule are frequently recognized too late, leading to extensive and costly counteractions and design changes. Consequently, robust and efficient construction execution requires real-time, transparent and accurate progress monitoring.In this research project, innovative and fundamental methods for automated progress monitoring of finishing works using video-based as-built data and 4D as-designed building information models (BIM) are developed. This includes, for example, methods for model-based position and orientation estimation of the camera in order to register videos within the 4D BIM. Consequently, building design information can directly be mapped onto the captured image data. On this basis, machine vision and learning methods are designed to detect and recognize relevant finishing items and their completion states. For this purpose, progress and viewing direction dependent object and state features are defined and stored in an extendable object catalogue. Additionally, novel concepts to store and link relevant position, geometry and material classifiers for finishing items based on the 4D BIM as well as their continuous adaption are the focus of this research. The results are expected to significantly improve the accuracy, the robustness, and the efficiency of progress monitoring for finishing works.