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

该项目的主要目标和新的方法概述如下。这项研究适用于计算机视觉、生物医学工程和数字建筑领域。研究的主要目标/目的：本研究将首先解决对象/曲面与动态点云数据(这些数据由对象上随机点随时间变化的3D空间坐标组成)的拟合问题。这些数据可以通过廉价的商用飞行时间摄像头或更昂贵的激光雷达系统获得。在飞行时间相机的情况下，自动将表面适配到呼吸患者的胸部(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，如音量和流量)进行区域分析，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观察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基元，并将它们与模型匹配(如果存在)。这项研究将与英国剑桥数字构建中心目前的CUD工作相配合。将在项目过程中实施的新的物理科学/工程方法：为了开展上述研究，我们预计将需要开发一些新的解决方案。首先，我们将研究将几何基元(平面等)和非均匀曲面(如胸部区域)拟合到动态点云数据的新方法。这将需要将计算机视觉和计算机图形技术与统计优化相结合。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。传统的卷积神经网络(CNN)方法需要对这些数据进行扩展，相关数据(其他已知信息)的融合也可能是重要的。

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.