Automated As-Is Modeling with Visual and Spatial Data Fusion

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The objective of this proposal is to evaluate whether a framework proposed by the applicant can reconstruct building interiors into an object-oriented model. According to the proposed framework, building interiors are first surveyed with a portable visual and spatial sensing system, which provides a stream of video frames along with per-pixel depth values. Building elements in the video frames are recognized based on their visual patterns. The occupying pixels are tagged with semantic labels, such as "column" or "wall". Meanwhile, a set of point-wise 3D correspondences between any two video frames are extracted by matching the locations of their joint visual features and evaluating the features' depth values. Based on these 3D correspondences, the pair-wise relative transformation between the frames is estimated. This way, the pixels in the frames can be aligned to form a "cloud" of 3D points. According to the labels previously assigned to the pixels, the points in the cloud can be divided into multiple parts and modeled parametrically. This object-oriented parametric model represents the perceived building interiors, and can be used for building interior construction and retrofit. The motivation behind this research lies in the manual and time-consuming nature of the current as-is modeling process. As-is models record building's existing conditions, which are useful for many assessment and management tasks, such as renovation/retrofit planning, maintenance scheduling, project quality control, etc. However, the current as-is modeling process requires a lot of manual editing and correction work, which could take up over two thirds of the overall modeling effort. The large volume of manual work increases modeling cost, and limits the use of as-is models in the vast majority of construction and retrofit projects. As a result, rework due to design and construction changes always happens, which costs up to 10.29% of the contract values. Any efforts towards automating this modeling process will increase the potential use of as-is models in civil projects and reduce unnecessary rework. Considering that construction was a $73.8 billion business in Canada in 2010, 1% of rework reduction would lead up to $75.9 million savings.