SBIR Phase I: Automatic Reconstruction of As-is Building Information Model from Indoor Point Cloud Data for Planning Purposes

这个小型企业创新研究（SBIR）第一阶段项目的更广泛影响将开发一个新的平台，自动化从云数据生成3D模型的过程。 几家公司已经开发了工具来促进建模过程，但是尽管他们的工具提供了好处，但是这个过程仍然是半自动的，昂贵的，耗时的，劳动密集型的，容易出错的，并且需要设计师。 拟议的项目将创建结构，建筑和机械部件的三维实体表示（例如，梁、天花板、柱、地板、管道、墙壁）;开口（例如，门，窗）;和家具（例如，沙发、床、椅子、桌子）。 此外，该平台将允许使用生成的模型进行可视化，协调和场景管理;简化规划任务;并改善沟通和协作。 拟议的解决方案将使很少或没有设计3D模型经验的用户能够提高生产力，减少规划时间和成本，并增加协作。这个小型企业创新研究（SBIR）第一阶段项目旨在开发一个平台，提供一个快速，简单，经济的解决方案，以生成室内场景的3D实体表示。该平台将使用人工智能（AI）来提取嵌入点云数据中的几何和语义信息，并拟合立体几何，以生成生活区、办公室、杂物间和机械室等室内场景的3D立体表示。 拟议平台的设计将包括三个主要目标：（1）自动生成三维实体模型，（3）使用模型进行规划的新工具，以及（3）与行业标准通信工具的集成。 第一项任务有三个主要研究目标： (a)多尺度特征提取，（B）通过机器学习算法对点云主要元素进行语义识别，以及（c）使用元素的图元和属性生成3D实体模型。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact of this Small Business Innovation Research (SBIR) Phase I project will develop a new platform automating the 3D model generation process from cloud data. Several companies have developed tools to facilitate the modeling process, but despite the benefits offered by their tools the process is still semi-automatic, expensive, time-consuming, labor-intensive, error-prone, and requires a designer. The proposed project will create 3D solid representations of structural, architectural, and mechanical components (e.g., beam, ceiling, column, floor, pipe, wall); openings (e.g., door, window); and furniture (e.g., sofa, bed, chair, table). Furthermore, the platform will allow use of the generated model for visualization, coordination, and scene management; simplify planning tasks; and improve communication and collaboration. The proposed solution will enable users with little or no experience designing 3D models to have the capability to increase productivity, reduce planning time and cost, and increase collaboration. This Small Business Innovation Research (SBIR) Phase I project aims to develop a platform that provides a quick, easy, and economical solution to generate 3D solid representations of indoor scenes. The platform will use artificial intelligence (AI) to extract the geometric and semantic information embedded in point cloud data and fit a solid geometry for generating the 3D solid representation of indoor scenes such as living areas, offices, utility rooms, and mechanical rooms. The design of the proposed platform will consist of three major objectives: (1) automatic generation of 3D solid models, (3) new tools to use the model for planning purposes, and (3) integration with industry-standard communication tools. The first task has three principal research objectives: (a) multi-scale feature extraction, (b) semantic identification of the point cloud main elements through machine learning algorithms, and (c) 3D solid model generation using the elements' primitives and attributes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.