Appearance-based material classification for monitoring of operation-level construction progress using 4D BIM and site photologs.

This paper presents a new appearance-based material classification method for monitoring construction progress deviations at the operational-level. The method leverages 4D Building Information Models (BIM) and 3D point cloud models generated from site photologs using Structure-from-Motion techniques. To initialize, a user manually assigns correspondences between the point cloud model and BIM, which automatically brings in the photos and the 4D BIM into alignment from all camera viewpoints. Through reasoning about occlusion, each BIM element is back-projected on all images that see that element. From these back-projections, several 2D patches are sampled per element and are classified into different material types. To perform material classification, the expected material type information is derived from BIM. Then the image patches are convolved with texture and color filters and their concatenated vector-quantized responses are compared with multiple discriminative material classification models that are relevant to the expected progress of that element. For each element, a quantized histogram of the observed material types is formed and the material type with the highest appearance frequency infers the appearance and thus the state of progress. To validate, four new datasets of incomplete and noisy point cloud models are introduced which are assembled from real-world construction site images and BIMs. An extended version of the Construction Material Library (CML) is also introduced for training/testing the material classifiers. The material classification shows an average accuracy of 92.4% for CML image patches of 100 × 100 pixels. The experiments on those four datasets show an accuracy of 95.9%, demonstrating the potential of appearance-based recognition method for inferring the actual state of construction progress for BIM elements. [ABSTRACT FROM AUTHOR]