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

三维场景数字化--一种新的大规模环境捕获的不变性方法。Aliaga，Mireille Boutin，Carl Cowen普渡大学大型真实世界环境的模拟是当今计算技术的核心挑战。应用范围广泛且多样。例如，它将使学生能够虚拟访问著名的历史遗迹，如博物馆，寺庙，战场和遥远的城市;土木工程师捕捉建筑物，并将其与原始设计或模拟进行比较（例如，比较建成模型和灾难前后的模拟模型）;考古学家虚拟地保存复杂的挖掘地点，如随着时间的推移而演变的战壕;士兵和消防队员在模拟环境中训练;真实的房地产经纪人向买家展示房屋的内部装饰;世界各地的人们都可以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，当前的采集方法执行得相当差。即使是小的场景，他们通常无法充分捕捉许多细节。手动创建的模型虽然很流行，但非常耗时，而且渲染图像对现实的表现力很差。或者，基于图像的建模和渲染产生真实感图像，但仅在从有限范围的视点看到的小和/或漫射环境的上下文中（例如，QuickTime VR）。类似地，专注于重建场景的几何形状的方法，如计算机视觉中开发的重建方法或激光扫描方法，与复杂的遮挡，镜面反射表面和大型数据集作斗争，因此，本提案的研究目标是开发捕获和操纵大型复杂现实世界场景的视觉丰富的计算机模型所需的算法。该建议攻击这个研究问题与一个新的混合方法相结合的几何和光度信息包含在场景中。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，并使用该冗余数据集来提取场景的表面几何形状和反射特性的精确模型。这是在对比与mostcurrent方法，其中一个获取稀疏的数据集，并使用方法来插入missinginformation。这项工作通过半自动平台导航、数据过滤和工作集管理等更简单的任务取代了插值。其关键是发展高效的数学数据处理技术，该方法的主要研究贡献是融合了数学和计算机科学的专业知识来解决当今计算技术中的难题.特别是，该研究利用了一种新的几何重建方法的基础上李群理论，这是最近开发的合作PI之一。该方法利用群作用的一组不变量来消除通常包含在三维重建问题中的一些多余的未知数。这些多余的未知量正是使重建方程非线性的未知量。通过去除它们，该方法最终得到一组简单的稀疏线性方程，其中包含最少数量的未知数，可以顺序求解。这使得该项目能够快速而稳健地提取大数据集的几何（和光度）信息，并重建大型3D环境。研究人员以前从未有机会获得如此大而密集的环境样本。除了出版物和提供所有软件外，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。

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.