Computational Photography for Capturing Virtual Environments

用于导航、训练和计算机动画的基于图像的虚拟环境很有吸引力，但生成高质量和物理上可信的大型环境仍然超出了当今的技术范围。仍然存在许多研究挑战，包括使基于图像的大型环境保持一致，尽管在捕获过程中光照发生变化，然后在不同的光照下显示它们。另一个主要挑战是赋予基于图像的环境一些交互的物理行为，例如，对交互作用力的响应。基于图像的虚拟环境必须变得更加丰富，才能提供这些新的功能，从而使计算机动画行业、移动导航和安全、资产监控和基于模拟的培训等领域能够实现新的应用。 拟议的研究通过改变捕获过程以将计算直接包括在图像采集中来解决这些挑战。捕获期间的计算可以引导成像过程来获取基于图像的环境的必要信息，例如通过在线跟踪表面或通过调整图像曝光来捕获正确曝光的所有图像区域。捕获过程的可扩展性将通过调查允许向现有环境添加新图像的增量方法来解决。增量式方法还有助于处理环境随时间演变的动态场景。这项研究将有极好的机会培养领域的高素质人员，许多加拿大中小型企业准备聘用这些人员。软件工程技能在当今的经济中至关重要，这项研究将培养出在现实世界的传感器和摄像机、计算机视觉和移动并行计算、当今图形处理单元(GPU)和CPU以及人机交互方面的软件专家，但它也将确保高素质的人才将获得基本的数学和物理背景，使他们脱颖而出，并确保他们长期成功。

Image-based virtual environments for navigation, training and computer animation are attractive, but generating high-quality and physically plausible large environments is still beyond today's technology. Many research challenges remain, including making large image-based environments consistent despite changes in illumination during capture and displaying them afterwards under different illumination. Another major challenge is to give image-based environments some interactive physical behaviour, e.g., a response to interactive forces. Image-based virtual environments must become richer to provide these new capabilities which will enable novel applications in the computer animation industry, in mobile navigation and in security, in monitoring of assets and in simulation-based training. The proposed research addresses these challenges by changing the capture process to include computation directly into the image acquisition. Computation during capture can steer the imaging process to acquire the necessary information for the image-based environment, e.g., through on-line tracking of surfaces or by adjusting image exposure to capture all image areas correctly exposed. Scalability of the capture process will be addressed by investigating incremental methods which allow to add new images to an existing environment. Incremental methods will also help with dynamic scenarios where the environment evolves over time. The research will have excellent opportunities to train highly-qualified personnel in fields with many Canadian small and medium enterprises ready to take-up this personnel. Software engineering skills are critically important in today's economy and this research will create software experts in real-world sensors and cameras, in computer vision and in mobile parallel computing on today's graphics processing units (GPU) and on the CPU, as well as in human computer interaction but it will also ensure that highly-qualified personnel will acquire the fundamental mathematical and physical background to set them apart and ensure their long-term success.