Reciprocal Reconstruction and Recognition for Modeling of Constructed Facilities

本项目的研究目的是评估pi提出的新框架是否可以逐步将钢筋混凝土框架结构重构为面向对象的几何模型，从而以经济有效的方式自动化已建成设施的建筑信息模型（BIM）制作过程。根据项目框架，建模师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。在这一阶段中，在生成的图像流中检测结构构件（本研究中的混凝土柱和梁），并在所有图像中标记其占用区域。利用这些区域在图像之间建立对象级的对应关系，有效地解决了粗糙配准问题。然后将来自运动的基于线的结构应用到结果中，以生成带有识别区域标记的结构的渲染3D视图。这循环回到结构成员的检测，现在也可以在视觉标记区域所覆盖的空间数据上执行。结果是更稳健的元素检测（通过结合视觉和空间检测结果），从而改进了元素匹配和重建。由此产生的面向对象模型预计将是具有检测到的承载线性构件的结构的精确3D表示。该模型提供给建模者，然后建模者可以使用它来完成模型制作过程。因此，该框架的关键智力价值在于其对结果的互惠使用；建筑元素的视频识别用于辅助其空间数据的三维重建，而三维重建提供空间识别所需的空间数据，以辅助更鲁棒的元素检测。这项工作将带来的直接优势是能够在已构建模型生成过程中自动化频繁元素的建模，这为建模者节省了大量的时间。美国国家工程院最近将恢复和改善城市基础设施列为21世纪工程的重大挑战之一。造成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，超过三分之二的工作量都花在手动将表面数据转换为3D模型上。其结果是，绝大多数新建筑和改造项目都没有生产成品模型，这导致返工和设计变更的成本高达安装成本的10%。任何对建模过程自动化的努力都将增加被建模的基础设施项目的百分比，考虑到建筑是一个价值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.