Enhancing Construction Site Analysis and Understanding with 3D Segmentation

TL;DR

This paper evaluates the effectiveness of advanced 3D segmentation models, SAM and Mask3D, in outdoor and indoor construction site environments to improve automation and accuracy in progress monitoring.

Contribution

It provides a comparative analysis of SAM and Mask3D in real-world construction settings, highlighting the need for specialized outdoor segmentation benchmarks.

Findings

SAM and Mask3D show varying performance in outdoor construction scenarios.

Both models require adaptation for effective outdoor site segmentation.

The study emphasizes the importance of tailored workflows for construction site data analysis.

Abstract

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.