In this paper a new approach that automatically recognizes progress from daily construction photologs and Building Information Models (BIMs) is presented. Currently daily site photologs are being collected at almost no cost on all construction sites; meanwhile BIM models are increasingly turning into binding components of AEC contracts. Using these emerging sources of information, we present a 4 Dimensional Augmented Reality - D4AR - modeling approach for integrated visualization of as-built and as-planned models as well as a novel framework for automated recognition of progress. Our approach is based on structure-from-motion, multi-view stereo plus voxel coloring and labeling algorithms to calibrate cameras, reconstruct the building scene, traverse and label the integrated as-built and as-planned scene for occupancy and visibility. Next, a machine learning scheme built upon a Bayesian model is proposed that automatically detects physical components in presence of occlusions and demonstrates that component-based tracking could be fully automated. Finally, the system enables the as-planned and as-built models to be jointly explored with an interactive, image-based 3D viewer where deviations are automatically color-coded over the BIM model. To that extent, we present our underlying hypotheses and algorithms for generation of integrated as-built and as-planned models plus automated progress monitoring which is the first of its kind that takes advantage of unordered daily site photologs. Experimental results are presented for challenging datasets collected under different lighting conditions and sever occlusions from two ongoing construction projects.