Deep Learning for Vision-based Measurement

基于视觉的测量的深度学习

• 批准号: RGPIN-2018-04405
• 负责人: Lang, Jochen
• 金额: $ 1.68万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=656573
• 关键词: Deep; Learning; Vision; based; Measurement

Deep learning techniques are remarkably successful on detection and recognition tasks in computer vision, reaching better than human performance in some specific applications. In my research I will develop novel learning-based algorithms and methods for virtual reality where data driven interaction and environment modelling, as well as user interfaces, can significantly benefit from advances in computer vision. Visual tracking allows different virtual reality and augmented reality devices to remain registered and synchronized. Motion estimation of surfaces, objects and actors is crucial in motion capture and 3D interaction modelling. Multi-view stereo captures real-word environments enabling navigation through these environment based on geometric relationships. My research program will significantly improve visual tracking, motion and stereo algorithms with learning-based techniques by focusing on vision based measurement. Vision based measurement uses the camera as a measurement instrument to obtain a measurand through an associated measurement procedure and with an uncertainty. This is crucial but often overlooked in virtual and augmented reality where different sensors and sensing results need to be fused with each other but also combined with physical reality. The geometry but also the appearance of captured objects and characters must not only appear realistic on their own but also when integrated into the whole virtual or augmented reality. In my research program, I will develop methods that on the one hand use physical constraints on the learning and on the other hand use learning to obtain physically plausible models. I will work on making consistent long-term tracking possible, develop real-time learning methods for motion estimation and 3D capture, thereby advancing the state-of-the-art in virtual and augmented reality through a focus on vision based measurement.

深度学习技术在计算机视觉的检测和识别任务上非常成功，在某些特定应用中达到了比人类更好的表现。在我的研究中，我将为虚拟现实开发新颖的基于学习的算法和方法，其中数据驱动的交互和环境建模以及用户界面可以从计算机视觉的进步中受益匪浅。视觉跟踪允许不同的虚拟现实和增强现实设备保持注册和同步。表面、物体和演员的运动估计对于运动捕捉和 3D 交互建模至关重要。多视图立体捕捉真实环境，从而能够根据几何关系在这些环境中进行导航。我的研究计划将通过专注于基于视觉的测量，通过基于学习的技术显着改进视觉跟踪、运动和立体算法。基于视觉的测量使用相机作为测量仪器，通过相关的测量过程获得具有不确定性的被测量。这一点至关重要，但在虚拟和增强现实中经常被忽视，其中不同的传感器和传感结果需要相互融合，但也需要与物理现实相结合。捕获的物体和角色的几何形状以及外观不仅必须本身真实，而且在集成到整个虚拟或增强现实中时也必须真实。在我的研究计划中，我将开发一些方法，一方面使用物理约束进行学习，另一方面使用学习来获得物理上合理的模型。我将致力于实现一致的长期跟踪，开发运动估计和 3D 捕捉的实时学习方法，从而通过专注于基于视觉的测量来推进虚拟和增强现实的最新技术。