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Point cloud denoising is a crucial and fundamental step in geometry processing, which has achieved significant progress in the last two decades. Denoising real-world noisy point clouds is a very challenging problem since it is hard to describe the complex real-world noise by simple distributions such as Gaussian distribution. Furthermore, existing methods may suffer from performance degradation when dealing with real-world noisy point clouds with complex structures, which contain not only sharp features (sharp edges, sharp corners, etc.) but also smooth features, fine features, etc. To solve the above-mentioned problems, we propose a novel structure-aware denoising approach by exploiting the prior information in both external clean point clouds and the given noisy point cloud. We first group nonlocal self-similarity (NSS) patches from a set of external clean point clouds. Then, we employ the Gaussian Mixture Model (GMM) learning algorithm to learn external NSS priors over patch groups. Next, the internal priors are learned from the given noisy point cloud in the same way to refine the prior model. We integrate both the learned external and internal priors into a set of orthogonal dictionaries to efficiently estimate point normals. Finally, we propose a feature-aware point updating method through adaptive neighborhood selection to reposition points to match the estimated normals. Extensive experiments show that our approach achieves favorable comprehensive performance compared with many popular or state-of-the-art methods in terms of both objective and visual perception. The source code can be found at https://zhiyongsu.github.io. • A structure-aware denoising method is proposed for real-world noisy point clouds. • The proposed method jointly learns external and internal priors for denoising. • A feature-aware point updating method is proposed. [ABSTRACT FROM AUTHOR]

Piping isometric drawings, which feature their intrinsical topological relation rather than just geometrical shape, are important industrial art works in the field of Computer-Aided Design (CAD). This paper takes a fresh look at the topology integrity authentication of piping isometric drawings, which has not been mentioned before in the literature, from the digital watermarking perspective. A blind and semi-fragile watermarking based algorithm is proposed to address the referred interesting issue. The topology authentication problem of piping isometric drawings is investigated. In addition to the stretching operation, both global and local similarity transformation operations, which are critical problems in the case of watermarking embedding and extraction, are analyzed in detail. The topological graph is extracted and constructed from the drawing firstly. Then, similarity transformation invariants are constructed as watermarks carriers for each node. After that, the topological relation among joint components is encoded into singular watermarks for each node of the graph. These generated topology sensitive watermarks are embedded into geometrical invariants of each node via quantization index modulation. Theoretical analysis and experimental results demonstrate that our approach yields a strong ability in detecting and locating unauthorized topology attacks while achieves robustness against both global and local similarity transformations especially the stretching operation. The proposed scheme can be employed to authenticate topology integrity for each of the drawings derived from the model individually in industry practices. [ABSTRACT FROM AUTHOR]