  Geometry processing of 3D objects is of primary interest in many areas of
computer vision and graphics, including robot navigation, 3D object
recognition, classification, feature extraction, etc. The recent introduction
of cheap range sensors has created a great interest in many new areas, driving
the need for developing efficient algorithms for 3D object processing.
Previously, in order to capture a 3D object, expensive specialized sensors were
used, such as lasers or dedicated range images, but now this limitation has
changed. The current approaches of 3D object processing require a significant
amount of manual intervention and they are still time-consuming making them
unavailable for use in real-time applications. The aim of this thesis is to
present algorithms, mainly inspired by the spectral analysis, subspace
tracking, etc, that can be used and facilitate many areas of low-level 3D
geometry processing (i.e., reconstruction, outliers removal, denoising,
compression), pattern recognition tasks (i.e., significant features extraction)
and high-level applications (i.e., registration and identification of 3D
objects in partially scanned and cluttered scenes), taking into consideration
different types of 3D models (i.e., static and dynamic point clouds, static and
dynamic 3D meshes).
