Reciprocal Reconstruction and Recognition for Modeling of Constructed Facilities

本项目的研究目标是评估由私人机构建议的新框架能否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设施的建筑信息模型(BIM)制作过程。根据该项目的框架，建模人员从所有可到达的角度对结构进行录像，以最大限度地减少遮挡。在此阶段，检测得到的图像流中的结构构件(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。利用这些区域在目标层次上建立图像间的对应关系，有效地解决了粗配准问题。然后，将来自运动的基于线的结构应用于结果，以产生具有所识别的标记区域的结构的渲染3D视图。这返回到结构构件的检测，现在还可以对视觉标记区域覆盖的空间数据执行检测。其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的模型预计将是结构的准确3D表示，并检测到承重线性构件。这个模型被提供给建模师，然后建模师可以使用它来完成模型制作过程。因此，该框架的关键智力优势在于其结果的互换使用；建筑元素的视频识别用于辅助其空间数据的3D重建，而3D重建提供空间识别所需的空间数据以帮助更稳健的元素检测。这项工作的直接优势将是能够在建成模型生成过程中自动对频繁元素进行建模，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世纪工程学的重大挑战之一。造成这一重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，需要在建筑中实现更多的自动化，以及缺乏可行的方法来绘制和标记现有的基础设施。为简单的基础设施建模所需的工作中，超过三分之二的工作都花在了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正在建模的基础设施项目的百分比，考虑到建筑业是一个价值9000亿美元的行业，每增加1%，就可以节省高达9亿美元的成本。

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.