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).

中文翻译：

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静态和动态 3D 几何的光谱处理和优化

3D 对象的几何处理是计算机视觉和图形的许多领域的主要兴趣，包括机器人导航、3D 对象识别、分类、特征提取等。 最近引入的廉价距离传感器引起了许多新领域的极大兴趣，推动开发用于 3D 对象处理的高效算法的需求。以前，为了捕捉 3D 对象，需要使用昂贵的专用传感器，例如激光或专用距离图像，但现在这种限制已经改变。当前的 3D 对象处理方法需要大量的人工干预，而且它们仍然很耗时，因此无法在实时应用程序中使用。本论文的目的是提出算法，主要受频谱分析、子空间跟踪等启发，