Progress monitoring is the backbone for construction project planning and control. It helps construction managers in keeping a check for timely project completion. There are several ways to monitor a project's progress; however, the widely used are traditional techniques that are manual, time-consuming, requires significant effort, and are prone to errors. Vision-based tools have great potential in progress monitoring but are being used sparsely due to a scarcity of domain knowledge and expertise. Previously, researchers and practitioners have explored pipelines for progress monitoring in automated construction, combining vision-based techniques and systems. These pipelines have appeared in the literature, each with its strength and weakness. Their evaluation is critical to clarify the status quo of the developments applied for a construction site and direct further research for improvements, which is the motivation for this study. This study reviews the existing literature, studies industry practices, and assemble an integral framework for computer vision-based construction progress monitoring (CV-CPM). Next, a novel method is proposed and validated by experimentation to measure the progress of a building by detecting geometrical components using the point intensity approach. It is expected that this research will provide implementable solutions to overcome the existing challenges in adopting vision-based progress monitoring for construction projects.