BIGDATA: Small: DA: Semantic Modeling of Cities from Scanned Data

详细的城市环境三维模型为许多应用提供了关键信息，包括应急准备、安全分析、城市规划和增强现实地图。例如，如果完整城市的3D模型是公开可用的，并且具有针对所有语义对象(例如，建筑物、消防栓、消防通道、门、窗、树等)的详细标签，则消防员、警察部队和其他应急响应团队可以使用它们来制定救援行动的计划，同时考虑到可能的接入点、视线和对邻里的风险。或者，如果3D模型包含停车灯、交通标志、停车位、商店位置、邮箱和自动取款机的标签表示，那么增强现实显示可以帮助人们在日常生活中导航。该项目的研究目标是开发算法，根据当前可用的数据构建详细的标签3D模型。几家公司(如谷歌、诺基亚、微软等)目前正在用安装在汽车上的扫描仪收集照片图像和激光雷达数据，这些扫描仪在世界各地的城市的街道上行驶。这些数据包含了关于我们世界的大量信息，但形式非常原始：像素和点。PI正在开发算法来分析这些原始数据以构建带语义标签的3D模型：1)发现不同数据类型之间对应关系的新方法，重点关注在在线存储库中发现的LIDAR、图像和3D多边形模型；2)基于从示例中学习的模型同时推断表面几何图形、分段和标签的新方法；3)允许用户通过将用户输入合并到增量更新的解决方案中来可视化和指导算法操作的新交互系统；以及4)用于多分辨率存储、压缩和检索海量扫描的3D数据集的数据管理算法。该项目的更广泛的目标包括教育项目、产业合作、免费分发软件和数据集，并开展外联活动。除了公布的研究成果外，PI还将发布主要城市的3D模型，这些模型可以直接用于其他人开发的应用程序。他还将分发代码、基准数据集和统计模型，这些可能会使各种学科的研究人员受益。这项拟议的工作与教育计划相结合，包括研究生、本科生和专业水平的跨学科研讨会和课程，以及促进弱势群体机会的多样性增强计划。

Detailed three-dimensional models of urban environments provide critical information for many applications, including emergency response preparation, security analysis, urban planning, and augmented-reality maps. For example, if 3D models of complete cities were publicly available with detailed labels for all semantic objects (e.g., buildings, fire hydrants, fire escapes, doors, windows, trees, etc.), then fire fighters, police forces, and other emergency response teams could use them to make plans for rescue operations, taking into account possible access points, lines of sight, and risks to the neighborhood. Or, if the 3D model contained labeled representations of stop lights, traffic signs, parking spaces, store locations, mailboxes, and ATMs, then augmented reality displays could help people navigate their daily lives.The research goal of this project is to develop algorithms to build detailed, labeled 3D models from currently available data. Several companies (e.g., Google, Nokia, Microsoft, etc.) are currently collecting photographic imagery and LIDAR data with scanners mounted on cars driving up and down streets of cities throughout the world. This data contains a vast amount of information about our world, but in a very primitive form: pixels and points. The PI is developing algorithms to analyze this raw data to build semantically labeled 3D models: 1) new methods for discovering correspondence relationships between heterogeneous data types, focusing on LIDAR, images, and 3D polygonal models found in online repositories, 2) new ways to infer surface geometry, segmentations, and labels simultaneously based on a model learned from examples, 3) new interactive systems to allow users to visualize and guide the algorithms as they operate by incorporating user input into incrementally updated solutions, and 4) data management algorithms for multiresolution storage, compression, and retrieval of massive scanned 3D data sets.The broader goals of the project include educational programs, industrial collaboration, free distribution of software and data sets, and outreach activities. Besides the published research results, the PI will disseminate 3D models of major cities that can be used directly in applications developed by other people. He will also distribute code, benchmark data sets, and statistical models that could benefit researchers in a variety of disciplines. This proposed work is integrated with educational programs, including interdisciplinary workshops and courses at the graduate, undergraduate, and professional levels, and diversity enhancement programs that promote opportunities for disadvantaged groups