Dynamic PoDynamic Point-Cloud Analysis: with int-Cloud Analysis: with applications in Thoracic Surface Reconstruction (TSR) and Autonomous Navigation

动态动态点云分析：与内部云分析：在胸部表面重建 (TSR) 和自主导航中的应用

• 批准号: 2108785
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2108785
• 关键词: Dynamic; PoDynamic; Point; Cloud; Analysis

The key aims of the project and novel methodology are summarised below. The research fits in to the computer vision, biomedical engineering and digital-build fields.1. Key Objectives/aims of research: This research will firstly address the problem of fitting objects/surfaces to dynamic point cloud data (such data consists of 3D spatial coordinates of random points on objects over time). This data can be obtained by inexpensive, commercially available time of flight cameras, or from more costly lidar systems. In the case of time of flight cameras, automatically fitting a surface to the thoracic (chest and abdomen) region of a breathing patient, will be one of our applications. A regional analysis of these breathing patterns (and parameters extracted, such as volume and flow) will enable us to automatically classify respiratory disorders (the aim is to look at motor neurone disease and childhood asthma -- with clinical collaborators).In the case of lidar, the point clouds are generally of a stationary object (normally a building) taken with a moving 'camera'. Here we aim to use the motion information to automatically and accurately segment building primitives and to match them to a model, if that exists. This research will tie in with current CUED work in the Cambridge Centre for Digital Build Britain.2. Novel physical sciences/engineering methodology which will be carried out during the course of the project:In order to carry out the research described above we anticipate that we will need to develop a number of novel solutions. Firstly, we will look into novel methods of fitting both geometric primitives (planes etc) and non-uniform surfaces (such as the thoracic region) to dynamic point cloud data. This will require integrating computer vision and computer graphics techniques with statistical optimisation. For the aspects which involve classification we hope to have a new (moving surface over time) dataset, to which we can apply machine learning methods. The conventional CNN (convolutional neural network) methods will require extensions for this data, and the fusion of related data (other known information) could also be important.

项目的主要目标和新方法总结如下。该研究适用于计算机视觉、生物医学工程和数字构建领域。本研究将首先解决物体/表面与动态点云数据的拟合问题（这些数据由物体上随机点随时间的三维空间坐标组成）。这些数据可以通过廉价的、商用的飞行时间照相机获得，也可以通过更昂贵的激光雷达系统获得。在飞行时间照相机的情况下，自动拟合一个表面到一个呼吸病人的胸部（胸部和腹部）区域，将是我们的应用之一。对这些呼吸模式（以及提取的参数，如体积和流量）的区域分析将使我们能够自动对呼吸系统疾病进行分类（目的是与临床合作者一起研究运动神经元疾病和儿童哮喘）。在激光雷达的情况下，点云通常是用移动的“相机”拍摄的静止物体（通常是建筑物）。在这里，我们的目标是使用运动信息来自动准确地分割建筑原语，并将它们与模型匹配（如果存在的话）。这项研究将与英国剑桥数字建筑中心目前的CUED工作相结合。将在项目过程中实施的新颖物理科学/工程方法：为了开展上述研究，我们预计我们将需要开发一些新颖的解决方案。首先，我们将研究将几何基元（平面等）和非均匀表面（如胸部区域）拟合到动态点云数据的新方法。这将需要集成计算机视觉和计算机图形技术与统计优化。对于涉及分类的方面，我们希望有一个新的（随时间移动的表面）数据集，我们可以应用机器学习方法。传统的CNN（卷积神经网络）方法需要对这些数据进行扩展，并且相关数据（其他已知信息）的融合也很重要。