IMAP: Image-based modeling with autonomous positioning

在过去的20年里，光学3D对象数字化系统有了显著的发展。目前有可能以低于毫米的精度和分辨率对各种物体的形状进行数字化。这类基于视觉的技术为工业检查、原型制作、设计和艺术、定制矫形器、考古文献以及更广泛的模拟等各个领域做出了重大贡献。加拿大是这一领域的世界领先者之一，几家服务提供商和设备制造商在过去10年中蓬勃发展。目前，设计者面临的主要挑战在于提高数字化系统的灵活性，并在捕捉表面外观方面实现更好的质量。这些挑战引发了计算机视觉领域的根本问题。首先，开发基于图像内容的智能自动参考系统应该同时处理捕获和整合数百张、甚至数千张必须相互参考的图像。该系统应提供基于自然和人工目标基准点的定位质量诊断。然后，除了用作表面外观捕获的工具外，还将利用该自定位系统来促进几何恢复。它不是从有限的一组图像中捕获纹理，而是使交互捕获对象的光场和反射特性成为可能，从而获得相对于当前纹理捕获和映射方法无与伦比的质量。该项目将为培养4名博士生和几名本科生做出贡献。该项目得到了一家专门从事3D数字化的公司以及加拿大国防研究和发展局移动机器人研究团队的支持。

Over the last 20 years, optical 3D object digitizing systems have evolved significantly. It is currently possible to digitize the shape of a wide range of objects with sub-millimetre accuracy and resolution. This type of vision-based technology has significantly contributed to various fields such as industrial inspection, prototyping, design and arts, tailor-made ortheses, archaeology documentation and, more generally, to simulation. Canada is among the world leaders in this field and several service providers and equipment manufacturers have blossomed in the last 10 years. Currently, the predominant challenges faced by designers consist in improving the flexibility of the digitizing systems and achieving better quality in capturing surface appearance. These challenges raise fundamental problems in the field of computer vision. First, developing an intelligent auto-referencing system based on image content should deal concurrently with capturing and integrating hundreds, and even thousands of images that must be referenced to each other. The system should provide a diagnosis of the positioning quality based on natural and artificial target fiducials. This self-positioning system will then be exploited for facilitating geometry recovery in addition to serving as a tool for surface appearance capture. Instead of capturing the texture from a limited set of images, it is expected to make interactive capture of light fields and reflectance properties of objects possible for obtaining incomparable quality with respect to current texture capture and mapping methods. This project will contribute to the training of 4 students at the doctorate level and a few undergraduate students. The project is supported by a company specialized in 3D digitizing as well as a research team in mobile robotics at Defence Research and Development Canada.