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Current assessment of progress in construction projects is a manual task that is often infrequent and error prone. Images of sites are extremely cluttered and rife with shadows, occlusions, equipment, and people -making them extremely hard to analyse. We present a first prototype system capable of detecting changes on a building site observed by a fixed camera, and classifying such changes as either actual structural events, or as unrelated. We exploit a prior building model to align camera and scene, thus identifying image regions where building components are expected to appear. This then enables us to home in on significant change events and verify the actual presence of a particular type of component. We place our approach within an emerging paradigm for integration in the construction industry, and highlight the benefits of automated image based feedback.

The addition of colour information to the computation of range/scene flow is proposed to improve its accuracy and robustness to ambiguities. This is applied in the form of additional optical flow constraints from aligned colour image data. Combining constraints gives improved velocity displacement fields for both synthetic and real datasets over using depth alone, or in using depth plus intensity. This ultimately has benefits for the processing of dense, temporal depth data obtainable from novel video-rate 3D capture systems.

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