MRI: Acquisition of Light Detection and Ranging (LIDAR) Scanner for Computer Vision Research and Interdisciplinary Education

该项目收购了徕卡ScanStation P30，旨在开发对齐二维(2D)和三维(3D)数据的方法，并使用产生的3D可视化来组装教育工具。LIDAR(光探测和测距)扫描仪将供该机构在计算机科学、电影和艺术领域学习和进行研究的教授和学生使用。该小组建立在研究人员现有工作的基础上，该工作利用注册了3D激光雷达扫描的2D图片和视频集创建可导航的3D环境。有了仪器，这个项目将实现以下相互交织的目标。(I)建立一个跨学科的研究小组，进一步研究如何描述和登记多模式数据，或通过不同的摄像机和传感器获得的数据。(Ii)使用开发的方法与电影和历史的合作者建立教育3D应用程序。(Iii)开发新的，使用拟议的扫描仪为许多学生提供接触最先进技术的跨学科课程。开发的课程将帮助调查员为她的研究实验室培训和招募学生，这些实验室可以从事跨学科项目。尽管目前收集和存储2D图像和3D LIDAR扫描可能更容易、更经济，但融合这种多模式数据仍然具有挑战性。在自动匹配方法中，由于在人造场景中经常出现的重复和歧义，以及同一对象的不同渲染上的各种属性，经常造成混淆。在照明条件和场景内容可能发生变化的一段时间内收集的图像集、不同的艺术渲染以及不同的传感器类型(相机、激光雷达等)都会导致数据集中的视觉变化。然而，通过结合背景信息来可视化直观地存在于每个图像来源中的区域属性，倡导者已经成功地解决了许多障碍。她的研究小组还将研究如何利用人的感知将3D LIDAR数据与其他类型的视觉信息配准。

This project, acquiring a Leica ScanStation P30, aims to develop methods for aligning two dimensional (2D) and three dimensional (3D) data and to assemble educational tools using the produced 3D visualizations. The LIDAR (LIght Detection And Ranging) scanner will be used by professors and students studying and performing research in the fields of computer science, film, and art at the institution. The group builds on the researcher's existing work that creates navigable 3D environments from sets of 2D pictures and videos that are registered with 3D LIDAR scans. With the instrumentation this project would enable the following intertwined goals.(i) Build an interdisciplinary research group to further study how to describe and register multi-modality data, or data obtained with different cameras and sensors.(ii) Use the developed methods to build educational 3D applications with collaborators in film and history.(iii) Develop new, cross-disciplinary courses that use the proposed scanner to provide access for many students to the state-of-the-art technology.The developed courses would help the investigator train and recruit students for her research lab that can work on interdisciplinary projects.Although at present collecting and storing both 2D images and 3D LIDAR scans might be easier and more economical, fusing such multi-modality data remains challenging. Confusion is often created in automatic matching methods by the repetition and ambiguity that often occur in man-made scenes as well as the variety of properties on different renderings of the same subject. Image sets collected over a period of time during which lighting conditions and scene content may have changed, different artistic rendering, as well as varying sensor types (Camera, LIDAR, etc.), can all contribute to visual variations in data sets. However, by incorporating contextual information to visualize the regional properties that intuitively exist in each imagery source, the proponent has successfully addressed many of the obstacles. Her research group will also study how human perception can be used for registering 3D LIDAR data with other types of visual information.