MSPA-MCS: Collaborative Research: New Methods for Robust, Feature-Preserving Surface Reconstruction

MSPA-MCS：协作研究：稳健、保留特征的表面重建的新方法

• 批准号: 0528209
• 负责人: Joseph S. Mitchell
• 金额: $ 20.51万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-15 至 2009-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0528209&HistoricalAwards=false
• 关键词: MSPA; MCS; Collaborative; Research; New

Digital scanning devices are capable of acquiring high-resolution 3D models and have recently become affordable and commercially available. Modeling detailed 3D shapes by scanning real physical models is becoming commonplace. Current scanners are able to produce very large amounts of raw, dense point sets; consequently, there has been a recent increase in the need for techniques for processing point sets. One of the principal challenges faced today is the development of surface reconstruction techniques that deal with the inherent noise of the acquired dataset. When the underlying surface contains sharp features, the requirement of being resilient to noise is especially challenging, since noise and sharp features are ambiguous, and most existing techniques tend to smooth important features or even to amplify noisy samples. The project helps to train young researchers to work at the intersection of graphics, geometry, and statistics, while enabling them to pursue theoretically sound work that has deep practical impact. The data, software, and models developed in this project will be disseminated for other researchers to use in benchmarking and testing.This research involves producing efficient and theoretically sound techniques for robust, feature-preserving surface reconstruction. It builds on recent work on the construction of a manifold surface from a set of points by using a moving least-squares (MLS) technique. The project explores the use of robust statistical techniques arising from outlier identification in MLS-based surface reconstruction. The approach is related to recent developments in feature-preserving smoothing, but it defines a surface rather than filtering the geometry. The techniques not only point to more reliable MLS projection but also extend the representation power of the underlying MLS surface definition to enable the representation of objects with sharp features.

数字扫描设备能够获取高分辨率的3D模型，并且最近已经变得负担得起并且商业上可用。 通过扫描真实的物理模型来建模详细的3D形状正变得越来越普遍。 目前的扫描仪能够产生非常大量的原始，密集的点集，因此，最近增加了对处理点集的技术的需求。 当今面临的主要挑战之一是开发处理所采集数据集的固有噪声的表面重建技术。 当底层表面包含尖锐特征时，对噪声具有弹性的要求尤其具有挑战性，因为噪声和尖锐特征是模糊的，并且大多数现有技术倾向于平滑重要特征，甚至放大噪声样本。 该项目有助于培训年轻的研究人员在图形，几何和统计的交叉点工作，同时使他们能够追求具有深刻实际影响的理论上合理的工作。 本项目中开发的数据、软件和模型将传播给其他研究人员用于基准测试和测试。本研究涉及产生有效的、理论上合理的技术，用于稳健的、保持特征的表面重建。 它建立在最近的工作上的一组点，通过使用移动最小二乘（MLS）技术的流形表面的建设。该项目探讨了使用强大的统计技术所产生的离群值识别基于MLS的表面重建。 该方法与特征保持平滑的最新发展有关，但它定义了一个曲面，而不是过滤几何体。这些技术不仅指向更可靠的MLS投影，而且还扩展了底层MLS表面定义的表示能力，以实现具有尖锐特征的对象的表示。