Recent technological advances in RGB-D sensing devices, such as the Microsoft Kinect,
facilitate numerous new and exciting applications, for example in 3D scanning [19] and human
motion tracking [20, 15, 5]. While affordable and accessible, consumer-level RGB-D
devices typically exhibit high noise levels in the acquired data. Moreover, difficult lighting
situations and geometric occlusions commonly occur in many application settings,
potentially leading to a severe degradation in data quality. This necessitates a particular
emphasis on the robustness of image and geometry processing algorithms. The
combination of 2D and 3D registration is one important aspect in the design of robust
applications based on RGB-D devices. This lecture introduces the main concepts of 2D
and 3D registration and explains how to combine them efficiently. An up-to-date version
of these course notes as well as implementation details and source code can be found at
http://lgg.epfl.ch/2d3dRegistration.