MSPA-MCS: 3D Scene Digitization - A Novel Invariant Approach for Large-Scale Environment Capture

MSPA-MCS：3D 场景数字化 - 一种用于大规模环境捕获的新颖的不变方法

• 批准号: 0434398
• 负责人: Daniel Aliaga
• 金额: --
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-15 至 2008-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0434398&HistoricalAwards=false
• 关键词: MSPA; MCS; 3D; Scene; Digitization

3D Scene Digitization A Novel Invariant Approach forLarge-Scale Environment CaptureDaniel G. Aliaga, Mireille Boutin, Carl CowenPurdue UniversityThe simulation of large real-world environments is a core challenge of computing technologytoday. Applications are numerous and diverse. For example, it would enable students to pay virtualvisits to famous historical sites such as museums, temples, battlefields, and distant cities; civilengineers to capture buildings and compare them to the original design or to simulations (e.g., tocompare as-built models and simulated models before and after a catastrophe); archeologists tovirtually preserve complex excavation sites such as trenches as they evolve over time; soldiers and firefighters to train in simulated environments; real estate agents to show buyers the interiors of homes;and, people all over the world to enjoy virtual travel or multi-player 3D games.Despite tremendous increases in computational power and storage space, current acquisitionmethods perform quite poorly. Even for small scenes, they usually fail to adequately capture manydetails. Manually created models, although popular, are extremely time-consuming and renderedimages are poor representations of reality. Alternatively, image-based modeling and rendering,produces photorealistic images but only in the context of small and/or diffuse environments seen froma limited range of viewpoints (e.g., QuickTime VR). Similarly, approaches which focus on recreatingthe geometry of the scene such as the reconstruction methods developed in computer vision or thelaser-scanning approaches struggle with complex occlusions, specular surfaces and large data sets.The research objective of this proposal is thus to develop the algorithms needed to capture andmanipulate visually rich computer models of large and complex real-world scenes. The proposalattacks this research problem with a new hybrid method combining both geometric and photometricinformation contained in the scene. More precisely, the approach captures a 3D environment bydensely sampling the space of viewpoints and uses this redundant data set to extract accurate modelsof the surface geometry and the reflectance properties of the scene. This is in contrast with mostcurrent approaches where one acquires a sparse set of data and uses methods to interpolate missinginformation. The work replaces interpolation by the easier tasks of semi-automatic platformnavigation, data filtering, and working-set management. The key is the development of highlyeffective mathematical data processing techniques.The main research contribution of the proposed approach is the merging of expertise from theMathematical and Computer Sciences to solve a difficult problem in computing technology today. Inparticular, the research makes use of a novel geometry reconstruction method based on Lie grouptheory which was recently developed by one of the co-PIs. This method uses a set of invariants of agroup action to eliminate a number of superfluous unknowns normally included in the 3Dreconstruction problem. These superfluous unknowns are exactly the ones that make thereconstruction equations nonlinear. By removing them, the method ends up with a simple set of sparselinear equations involving a minimum number of unknowns which can be solved sequentially. Thisallows the project to quickly and robustly extract the geometric (and photometric) information of largedata sets and reconstruct large 3D environments.The proposed research will have impact beyond the immediate reconstruction results. Neverbefore have researchers had access to such large and dense samplings of environments. Aside frompublications and making all software available, the research project will create a public repository tostore models for subsequent study (e.g., historically significant locations). The impact of the proposedwork is not an incrementally better method for capturing environments, but a bold new approach thatcan significantly change how people think about computer simulation of large environments.

3D 场景数字化 大规模环境捕获的新颖不变方法 Daniel G. Aliaga、Mireille Boutin、Carl Cowen 普渡大学大型现实世界环境的模拟是当今计算技术的核心挑战。应用程序多种多样。例如，它将使学生能够虚拟参观博物馆、寺庙、战场和遥远城市等著名历史遗迹；土木工程师捕获建筑物并将其与原始设计或模拟进行比较（例如，比较灾难前后的竣工模型和模拟模型）；考古学家可以虚拟地保存复杂的挖掘地点，例如随着时间的推移而演变的战壕；士兵和消防员在模拟环境中进行训练；房地产经纪人向买家展示房屋的内部装饰；以及世界各地的人们享受虚拟旅行或多人 3D 游戏。尽管计算能力和存储空间大幅增加，但当前的获取方法表现相当差。即使对于小场景，它们通常也无法充分捕捉到许多细节。手动创建的模型虽然很流行，但非常耗时，而且渲染的图像不能很好地反映现实。或者，基于图像的建模和渲染可以生成逼真的图像，但仅限于从有限的视点范围（例如 QuickTime VR）看到的小型和/或分散的环境中。同样，专注于重建场景几何形状的方法，例如计算机视觉中开发的重建方法或激光扫描方法，都难以应对复杂的遮挡、镜面反射表面和大数据集。因此，本提案的研究目标是开发所需的算法捕捉和操作大型且复杂的现实世界场景的视觉丰富的计算机模型。该提案采用一种新的混合方法来解决这个研究问题，该方法结合了场景中包含的几何和光度信息。更准确地说，该方法通过对视点空间进行密集采样来捕获 3D 环境，并使用此冗余数据集来提取表面几何形状和场景反射特性的准确模型。这与大多数当前方法形成鲜明对比，当前方法获取一组稀疏数据并使用方法来插入缺失信息。这项工作用半自动平台导航、数据过滤和工作集管理等更简单的任务取代了插值。关键是开发高效的数学数据处理技术。所提出的方法的主要研究贡献是融合数学和计算机科学的专业知识，以解决当今计算技术中的难题。特别是，该研究利用了一种基于李群理论的新颖几何重建方法，该方法是由一位共同PI最近开发的。该方法使用一组群动作的不变量来消除通常包含在 3D 重建问题中的许多多余的未知数。这些多余的未知数正是导致重构方程非线性的原因。通过删除它们，该方法最终得到一组简单的稀疏线性方程，其中涉及可以顺序求解的最小数量的未知数。这使得该项目能够快速、稳健地提取大型数据集的几何（和光度）信息并重建大型 3D 环境。所提出的研究将产生超出直接重建结果的影响。研究人员以前从未获得过如此大规模和密集的环境样本。除了出版物和提供所有软件之外，该研究项目还将创建一个公共存储库来存储模型以供后续研究（例如，具有历史意义的地点）。所提出的工作的影响不是一种逐渐更好的捕获环境的方法，而是一种大胆的新方法，可以显着改变人们对大型环境的计算机模拟的看法。