A New Pipeline for Detailed Large Scale Geometry Acquisition and Analysis

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3D acquisition is often compared with its 2D counterpart: while 2D acquisition is better, cheaper and more versatile than ever yielding very large datasets and a democratization of image processing research, unfortunately not the same can be said of 3D acquisition: It still requires expensive equipment, significant post-processing and operator training. Furthermore, accurate large-scale acquisition requires significant planning, logistics and automation that currently does not have efficient solutions. This is not for a lack of demand as 3D acquisition market is projected to exceed 6.22 billion USD by 2023 and yet, scanning large objects or areas with high resolution is still very expensive and time consuming despite having practical applications in nearly all fields: AR/VR, robotics, manufacturing , quality control and testing, civil engineering, infrastructure inspection, aeronautics industry, defense, mining, climate change to name a few. In this program we are planning to bridge the gap and propose new methodology for the 3D acquisition pipeline that can bring the elusive 3D acquisition pipeline into the mainstream, both consumer as well as industrial mainstream. We propose new technologies from improving sensor reliability and accuracy, for stitching together thousands of geometric pieces into one unified model, planning and automation of the 3D acquisition using autonomous agents such as drones and/or robots. We propose methods that can do 3D acquisition of objects that undergo small deformations during scanning such as from people breathing during acquisition or animals moving or objects sch as trees being deformed by wind. Time lapse scanning of objects can provide a unique insight into the physical structures scanned as well as the type of forces that might have been applied to them over time. We propose a novel reverse physics framework where using the FEM methods a smart difference operator can be applied that detects structural changes in terms of missing or added geometry (i.e. a piece of the objects fell, or a part of the object bulged) as well as in terms of elastic and plastic deformation and predict further the deformation trajectory. This can be very useful for infrastructure maintenance, where acquisition automation and accurate analysis can help detect early serious problems. In addition to the direct use of these technologies in a fast-growing market, this work has important human safety application in civil engineering and mining such as civil infrastructure maintenance, interior of nuclear power plants and exploring mines. Additionally, it will generate new research and unique datasets that can be further used to solve other problems thus the community and Canadian industry as a whole will greatly benefit from this research.