用3D分割提升工地监控效率，解决复杂环境下的识别难题
Enhancing Construction Site Analysis and Understanding with 3D Segmentation
- 对比SAM与Mask3D在室内外施工场景的分割表现
- 发现现有模型在室外动态环境中效果显著下降
- 为工地自动化监控提供可落地的分割方案
施工进度监控至关重要却耗时耗力，促使研究者探索基于计算机视觉的方法以提升效率和可扩展性。传统数据采集方法主要针对室内环境，在工地这种复杂、杂乱且动态变化的条件下表现不佳。本文系统评估了两种先进的3D分割方法——Segment Anything Model（SAM）和Mask3D——在真实施工场景中的适应性与性能。两模型均在室内数据集上训练，但在室内外复杂环境下表现出明显差距，凸显当前分割方法缺乏面向室外场景的基准测试。通过对比分析，本研究不仅展示了SAM与Mask3D的相对有效性，更强调了开发专用于施工场景的分割工作流的必要性，以从现场数据中提取可操作的洞察，推动施工监控向更自动化、精确的方向发展。
原文摘要 · Abstract (English)
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.
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