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Geometry processing of 3D objects is of primary interest in many areas ofcomputer vision and graphics, including robot navigation, 3D objectrecognition, classification, feature extraction, etc. The recent introductionof cheap range sensors has created a great interest in many new areas, drivingthe need for developing efficient algorithms for 3D object processing.Previously, in order to capture a 3D object, expensive specialized sensors wereused, such as lasers or dedicated range images, but now this limitation haschanged. The current approaches of 3D object processing require a significantamount of manual intervention and they are still time-consuming making themunavailable for use in real-time applications. The aim of this thesis is topresent algorithms, mainly inspired by the spectral analysis, subspacetracking, etc, that can be used and facilitate many areas of low-level 3Dgeometry 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 3Dobjects in partially scanned and cluttered scenes), taking into considerationdifferent types of 3D models (i.e., static and dynamic point clouds, static anddynamic 3D meshes).

Spectral Processing and Optimization of Static and Dynamic 3D Geometries