Enhancing Construction Site Analysis and Understanding with 3D Segmentation
It addresses the problem of resource-intensive construction progress monitoring for industry practitioners, but is incremental as it applies existing methods to a new domain without major innovations.
This paper evaluated the Segment Anything Model (SAM) and Mask3D for 3D segmentation in construction sites, finding that both models, originally trained on indoor data, face challenges in outdoor conditions due to a lack of benchmarks, with comparative analysis highlighting their relative effectiveness.
Monitoring construction progress is crucial yet resource-intensive, prompting the exploration of computer-vision-based methodologies for enhanced efficiency and scalability. Traditional data acquisition methods, primarily focusing on indoor environments, falter in construction site's complex, cluttered, and dynamically changing conditions. This paper critically evaluates the application of two advanced 3D segmentation methods, Segment Anything Model (SAM) and Mask3D, in challenging outdoor and indoor conditions. Trained initially on indoor datasets, both models' adaptability and performance are assessed in real-world construction settings, highlighting the gap in current segmentation approaches due to the absence of benchmarks for outdoor scenarios. Through a comparative analysis, this study not only showcases the relative effectiveness of SAM and Mask3D but also addresses the critical need for tailored segmentation workflows capable of extracting actionable insights from construction site data, thereby advancing the field towards more automated and precise monitoring techniques.