Reciprocal Reconstruction and Recognition for Modeling of Constructed Facilities

本项目的研究目的是评估由PI提出的新框架是否可以逐步将钢筋混凝土框架结构重建为面向对象的几何模型，以便以具有成本效益的方式自动化已建成设施的建筑信息模型（BIM）制作过程。根据该项目的框架，建模者从所有可访问的角度对结构进行录像，以最大限度地减少遮挡。在此阶段中，结构构件（本研究中的混凝土柱和梁）在所得到的图像流中被检测到，并且在所有图像中标记出它们的占用区域。这些区域用于在图像之间建立对象级别的对应关系，并有效地解决粗配准问题。然后将来自运动的基于线的结构应用于结果，以产生具有标记的识别区域的结构的渲染3D视图。这循环回到结构构件的检测，其现在也可以在由视觉标记区域覆盖的空间数据上执行。结果是更鲁棒的元素检测（通过组合视觉和空间检测结果），并且因此改进了元素匹配和重建。由此产生的面向对象的模型预计将是一个准确的三维表示的结构与承载线性构件检测。这个模型被提供给建模者，然后建模者可以使用它来完成模型制作过程。因此，这一框架的主要知识价值在于其成果的相互利用;建筑物单元的视频识别用于辅助其空间数据的3D重建，而3D重建提供了空间识别所需的空间数据，以帮助更强大的元素检测。这项工作的直接优势是能够自动化频繁的建模，在构建模型的生成过程中，这些元素可以为建模人员节省大量的时间。美国国家工程院最近将恢复和改善城市基础设施列为世纪工程的重大挑战之一。导致这一巨大挑战的两个最大问题是，通过计算机科学和机器人技术的进步，建筑需要更多的自动化，以及缺乏可行的方法来绘制和标记现有的基础设施。即使是简单的基础设施建模所需的工作量中，也有超过三分之二用于手动将表面数据转换为3D模型。其结果是，绝大多数新的建筑和改造项目都没有生产竣工模型，这导致返工和设计更改，成本高达安装成本的10%。任何自动化建模过程的努力都将增加基础设施项目的建模比例，考虑到建筑业是一个价值9000亿美元的行业，每增加1%就可以节省9亿美元。

The research objective of this project is to evaluate whether a novel framework proposed by the PIs can progressively reconstruct a reinforced concrete frame structure into an object-oriented geometric model, for the purpose of automating the Building Information Model (BIM) making process of constructed facilities in a cost-effective manner. According to the project's framework, the modeler videotapes the structure from all accessible angles to minimize occlusions. During this stage, the structural members (concrete columns and beams in this study) in the resulting stream of images are detected and their occupying region is marked in all images. These regions are used to establish correspondence at the object level across images, and solve the rough registration problem efficiently. Line-based structure from motion is then applied to the result to produce a rendered 3D view of the structure with the recognized regions marked. This loops back to the detection of structural members, which can now be also performed on the spatial data covered by the visually marked regions. The result is more robust element detection (by combining visual and spatial detection results), and consequently improved element matching and reconstruction. The resulting object-oriented model is expected to be an accurate 3D representation of the structure with the load bearing linear members detected. This model is provided to the modeler, who can then use it to complete the model making process. As a result, the key intellectual merit of this framework lies in its reciprocal use of the results; the video recognition of building elements is used to assist the 3D reconstruction of their spatial data, while the 3D reconstruction provides the spatial data needed for spatial recognition to assist in more robust element detection.The immediate advantage that will result from this work is the ability to automate the modeling of frequent elements during the as-built model generation process, which translates to tremendous time savings for the modeler. The National Academy of Engineering recently listed Restoring and Improving Urban Infrastructure as one of the Grand Challenges of Engineering in the 21st century. Two of the greatest issues that cause this grand challenge are the need for more automation in construction, through advances in computer science and robotics, and the lack of viable methods to map and label existing infrastructure. Over two thirds of the effort needed to model even simple infrastructure is spent on manually converting surface data to a 3D model. The result is that as-built models are not produced for the vast majority of new construction and retrofit projects, which leads to rework and design changes that cost up to 10 percent of the installed costs. Any efforts towards automating the modeling process will increase the percentage of infrastructure projects being modeled and, considering that construction is a $900 billion industry, each 1 percent of increase can lead up to $900 million in savings.