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High Quality 3D Geometry and Appearance Reconstruction of Non-Rigidly Deforming Objects using Low-Cost RGB-D Cameras

使用低成本 RGB-D 相机对非刚性变形物体进行高质量 3D 几何和外观重建

基本信息

批准号：
1806028
负责人：
金额：
--
依托单位：
CARDIFF UNIVERSITY
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-1806028
关键词：
Quality 3D Geometry Appearance Reconstruction

项目摘要

Capturing and reconstruction of high quality 3D geometry and the appearance of non-rigidly deforming objects, such as the dynamics of human actions, is essential for many applications, including movie and game production in the creative industries, Virtual Reality (VR) videoconferencing, analysing human behaviour for healthcare monitoring and sports analysis etc. Despite great effort, it is still a challenging problem, especially when large-scale deformations are involved: self-occlusion, subtle geometry and appearance change (e.g. wrinkles of skin and clothing) all contribute to the difficulty. This research aims to advance the state of the art by investigating novel data-driven techniques to address fundamental challenges. Low-cost RGB-Depth cameras have become more capable in recent years and will be used in the research to make the techniques widely useful. We will develop a new joint representation and analysis technique for both geometry and appearance, to effectively encode geometric and appearance change during non-rigid deformation. The plausible deformation and change in appearance typically form a low dimensional manifold embedded in this joint space. To address the issues of noise and incompleteness in the scanned data, machine learning techniques such as manifold learning will be exploited. This will effectively utilise information from any previous scans to fill the gaps and improve the quality of reconstruction. An optimisation framework will also be developed incorporating knowledge from the manifold as well as sparse priors.To make the research feasible, the project is built on top of the supervisors' existing work on shape deformation (representation and shape space analysis), non-rigid registration, data-driven reconstruction and sparse models, all of which have recent publications in top journals. The project is related and complementary to two current Royal Society international collaborative projects with leading research institutions in China. The research and the PhD student will benefit greatly from these collaborations.

捕捉和重建高质量的 3D 几何形状以及非刚性变形物体的外观（例如人类动作的动态）对于许多应用至关重要，包括创意产业中的电影和游戏制作、虚拟现实 (VR) 视频会议、分析人类行为以进行医疗保健监测和运动分析等。尽管付出了巨大的努力，但这仍然是一个具有挑战性的问题，特别是当涉及大规模变形时：自遮挡、微妙的变形几何形状和外观变化（例如皮肤和衣服的皱纹）都会增加难度。这项研究旨在通过研究新颖的数据驱动技术来解决根本挑战，从而推进最先进的技术。近年来，低成本 RGB 深度相机的功能变得越来越强大，并将用于研究以使该技术得到广泛应用。我们将开发一种新的几何和外观联合表示和分析技术，以有效地编码非刚性变形期间的几何和外观变化。合理的变形和外观变化通常形成嵌入在该联合空间中的低维流形。为了解决扫描数据中的噪声和不完整性问题，将利用流形学习等机器学习技术。这将有效地利用以前扫描的信息来填补空白并提高重建质量。还将开发一个结合来自流形和稀疏先验的知识的优化框架。为了使研究可行，该项目建立在导师现有的形状变形（表示和形状空间分析）、非刚性配准、数据驱动重建和稀疏模型工作的基础上，所有这些工作最近都在顶级期刊上发表。该项目与英国皇家学会当前与中国领先研究机构的两个国际合作项目相关且互补。研究人员和博士生将从这些合作中受益匪浅。