Computer vision developments to support circular economic activity in the built environment

建筑的建造和拆除占我们废物流的40%，而建筑在我们的实际能源和资源使用量中所占的比例也相当。因此，在建筑环境中走向循环经济（封闭的材料循环，减少浪费和增加再利用）对于我们的持续健康是必要的。然而，为了实现这一目标，必须克服重大的数字障碍，例如自动化数字孪生开发和资产表征。实现这些数字进步需要在理解如何有效地调解之间取得突破：(1)3D建筑模型（这是抽象的），以及(2)真实建筑及其元素在其建造和解构周期的各个阶段的3D扫描和感知信息。这形成了拟议项目的长期目标，其中数字世界中的规划、分析和虚拟工作将显著降低现实世界中建筑和解构的风险（和浪费）。

Building construction and demolition comprise 40% of our waste stream, and buildings account for similar amounts of our embodied energy and resource usage. Thus, moving toward a circular economy (closed materials loops, reduced waste and increased reuse) in the built environment is necessary for our sustained well being. However, significant digital barriers must be overcome to meet this goal, such as automated digital twin development and asset characterization. Enabling these digital advances requires breakthroughs in understanding how to effectively mediate between: (1) 3D building models (which are abstractions), and (2) the 3D scanned and sensed information of real buildings and their elements at stages in their construction and deconstruction cycles. This forms the long-term goal of the proposed program, in which planning, analysis and virtual work in the digital world will significantly reduce risk (and waste) in the real world of construction and deconstruction. Facilitating this breakthrough in the near term requires solving three sets of problems related to a class of computer vision algorithms that focus on deriving useful information from large 3D point clouds, related sensor data, and context. These problems share many underlying mathematical operations and data structures for fitting, feature matching, transformations, feature extraction, principle component analysis and ordered queries. Thus, over the next five years, the proposed program will pursue a unified approach in algorithm development for: (1) data fusion for semantically enriched, multidimensional 3Dscan-to-3Dmodel transformations (required for adaptive reuse project planning, asset management, and selective disassembly programing and optimization), (2) real-time built-object detection and locating in 3D point clouds (required for tracking components, dimensional fabrication control, and selective modular assembly planning), and (3) fitting and packing optimization for 3D scanned, irregular 3D objects (required for lowering carbon footprint of construction assembly shipments, designing with reused construction materials, and large scale printing volume optimization). Ultimately, the program will build new knowledge in the interdependencies among and circular economic applications of computer vision, mixed reality, serious gaming, generative design, human cognition, deep learning and robotics. Diverse trainees will thus acquire the knowledge and skills to adapt computer vision and modeling principles and methods to develop new algorithm classes for the built environment challenges ahead of us, filling critical skills and knowledge gaps in the Canadian construction, asset management and architectural engineering industries. Achieving these objectives will contribute to overcoming the digital barriers to a circular economy within the built environment, thus contributing to the wealth and well being of all Canadians.