Probabilistic scanning for high accuracy hand-held 3D reconstruction

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This grant proposal is in the field of computer vision. Its primary focus is on developing new active 3D reconstruction technologies which are highly accurate, fast, and robust to variations in material properties such as inter-reflection (glossy materials) and sub-surface scattering (translucent materials). 3D active scanning has been around for a long time, but it has recently gained new momentum because of new applications such as 3D mapping of cities, gaming controls (Kinect), and the increasing accessibility of 3D printing. The goal is to create new structured light algorithms suitable for a hand-held portable scanner, where portability will not compromise accuracy and robustness. Applications of hand-held scanning technology are numerous and range from industrial reverse engineering to quality control, damage assessment, or shape estimation for medical use. Fundamental algorithmic advances will be closely matched by applications development, such as scene modelling for adaptive and architectural projections in technological arts, and scanning for 3D printing. With the democratization of 3D printing, a need is emerging for practical reverse engineering solutions to allow scanning of various objects efficiently and accurately. Also, automated print quality control and printer calibration are currently difficult to achieve, since 3D printers create plastic objects which are inherently hard to scan because they use glossy or translucent materials. In order to develop fully, the field of 3D printing will require very accurate, robust, fast, flexible and low cost methods for scanning printed objects (during the printing process, if possible) as well as real objects. Current scanner technologies can not achieve all of these requirements, so this is where this grant intends to contribute by exploring new approaches to further advance the field of 3D scanning. Obviously, there are many ways to perform 3D scanning, but we consider that the most useful and promising direction is toward real-time hand-held scanners. In exchange for the flexibility, portability and efficiency they offer, hand-held scanners suffer from reduced accuracy and robustness. This is mostly due to the fact that these scanners need not only to establish matches for 3D triangulation, but also accurately track their own position in space as the scan proceeds. This research will proceed from our most recent results where a new probabilistic approach was proposed for establishing dense correspondence using a new form of structured light. The various sub-topics involved in this research are spatial coherence analysis, probabilistic high-dimension matching algorithms, incremental probabilistic scanning, multi-camera systems, and architectural multi-projector systems.