Adaptive compression of animated meshes by exploiting orthogonal iterations

Lalos, Aris S.; Vasilakis, Andreas A.; Dimas, Anastasios; Μουστάκας, Κωνσταντίνος

doi:10.1007/s00371-017-1395-4

Cited by 13 publications

(4 citation statements)

References 29 publications

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“…In this case, the denoising process can run for all frames concurrently because no information of the previous frames is required (except of the matrices

which are estimated once during the OI process applied only to the first frame). Additionally, adaptive compression of animated meshes could be used for real-time scenarios, as described in [ 44 ].…”

Section: Case Studiesmentioning

confidence: 99%

Spectral Processing for Denoising and Compression of 3D Meshes Using Dynamic Orthogonal Iterations

2020

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Recently, spectral methods have been extensively used in the processing of 3D meshes. They usually take advantage of some unique properties that the eigenvalues and the eigenvectors of the decomposed Laplacian matrix have. However, despite their superior behavior and performance, they suffer from computational complexity, especially while the number of vertices of the model increases. In this work, we suggest the use of a fast and efficient spectral processing approach applied to dense static and dynamic 3D meshes, which can be ideally suited for real-time denoising and compression applications. To increase the computational efficiency of the method, we exploit potential spectral coherence between adjacent parts of a mesh and then we apply an orthogonal iteration approach for the tracking of the graph Laplacian eigenspaces. Additionally, we present a dynamic version that automatically identifies the optimal subspace size that satisfies a given reconstruction quality threshold. In this way, we overcome the problem of the perceptual distortions, due to the fixed number of subspace sizes that is used for all the separated parts individually. Extensive simulations carried out using different 3D models in different use cases (i.e., compression and denoising), showed that the proposed approach is very fast, especially in comparison with the SVD based spectral processing approaches, while at the same time the quality of the reconstructed models is of similar or even better reconstruction quality. The experimental analysis also showed that the proposed approach could also be used by other denoising methods as a preprocessing step, in order to optimize the reconstruction quality of their results and decrease their computational complexity since they need fewer iterations to converge.

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“…In this case, the denoising process can run for all frames concurrently because no information of the previous frames is required (except of the matrices

Section: Case Studiesmentioning

confidence: 99%

Spectral Processing for Denoising and Compression of 3D Meshes Using Dynamic Orthogonal Iterations

2020

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“…∈ 3k×n is constructed from the k sequential frames. Common Dynamic mesh compression approaches are based on the application of Principal Components Analysis (PCA) to the animation matrix M [20]- [22]. Considering that M = U U T corresponds to the SVD of the animation matrix, it is reasonable to assume that M can be adequately approximated by using fewer principal components, due to the underlying spatiotemporal coherences.…”

Section: Dynamic Geometries: Low-rank Approximations and Pca Basedmentioning

confidence: 99%

“…In machine learning, the sparsity principle is used to automatically select a simple model among a large collection of them [26], while in signal processing, sparse modeling is employed to capture the ability of data to be expressed as linear combinations of a few atoms from a pre-described dictionary [27]. These models provide reasonable ways for exploiting the rich spatiotemporal structure, of the captured static and dynamic 3D geometry, using various statistical learning paradigms and well-documented merits, including PCA [20], [22], Dictionary Learning [28], Compressive Sampling (CS) [21], [29], [30] and Matrix Completion (MC) [31], to name a few.…”

Section: Sparse Modeling For 3d Meshes In a Nutshellmentioning

confidence: 99%

“…Similarly, Yoon et al [28] apply dictionary learning to get a few representative features that can be used to identify local patches from large-scale geometry data. Thanou et al [48] and Lalos et al [22] address the problem of compression of 3D point cloud sequences that are characterized by moving 3D positions. The fundamental theories presented in this tutorial will be essential for achieving improved compression rates offering at the same time low computational complexity [49].…”

Section: Compression Of Static and Dynamic Geometriesmentioning

confidence: 99%

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Signal Processing on Static and Dynamic 3D Meshes: Sparse Representations and Applications

et al. 2019

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Nowadays, real-time 3D scanning and reconstruction becomes a requirement for a variety of interactive applications in various fields, including heritage science, gaming, engineering, landscape topography, and medicine. From the introduction of 3D scanning, which allowed the representation of real world or synthetic objects into the virtual world, hardware and software advances have seen tremendous progress. However, despite the continuous improvement of the new generation image sensors and acquisition techniques, the acquired data are often corrupted by the low-frequency noise, outliers, misalignment, missing data, and variations in point density. These effects are amplified if the low-cost sensors and hardware are being used (e.g., mobile devices); thus, the acquisition and communication cost per datum is driven to a minimum. This paper provides a comprehensive review of the ongoing efforts in geometry and signal processing, describing several models from a wide range of signal processing relevant tasks, such as robust principal component analysis, compressive sampling, and matrix completion. Various scalable architectures and optimization algorithms are analyzed and reviewed, revealing significant insights into the fundamental processing operations and the involved implementation tradeoffs. Moreover, the impact of sparse modeling and optimization tools to several 3D mesh processing tasks, such as completion of missing data, feature preserving noise removal, and rejection of outliers, is illustrated via test cases with several constraints posed by the arbitrarily complex animated scenarios. Finally, the identified limitations together with the potential open research directions are also presented for future research efforts toward modeling and optimization for static and dynamic 3D models. INDEX TERMS Signal processing on static and dynamic meshes, sparse representation theory & algorithms, 3D geometry acquisition and processing.

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CGI 2017 Editorial (TVCJ)

2017

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Welcome to the special issue of the 34th Computer Graphics International conference (CGI'17)! Computer Graphics International is one of the oldest true international conferences in computer graphics. It is the official conference of the Computer Graphics Society (CGS), a long-standing international computer graphics organization. CGI and CGS were initiated in Tokyo by Professor Tosiyasu L. Kunii from the University of Tokyo in 1983. Since then, the CGI conference has been held annually in many different countries across the world and gained a reputation as one of the key conferences for researchers and practitioners to share their achievements and discover the latest advances in this field. CGI this year has been organized by Faculty of Science and Technology, Keio University in Yokohama, Japan, between 27th and 30th June, 2017, with support from CGS and in cooperation with ACM/SIGGRAPH and Eurographics. This special issue of the Visual Computer is composed of the 35 best papers from CGI'17. We received 171 submissions from 33 countries and regions, making the acceptance ratio about 20%. To ensure the highest quality of publications, each paper has been reviewed by at least three experts in the field. The International Program Committee is comB Xiaoyang Mao

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Adaptive compression of animated meshes by exploiting orthogonal iterations

Cited by 13 publications

References 29 publications

Spectral Processing for Denoising and Compression of 3D Meshes Using Dynamic Orthogonal Iterations

Spectral Processing for Denoising and Compression of 3D Meshes Using Dynamic Orthogonal Iterations

Signal Processing on Static and Dynamic 3D Meshes: Sparse Representations and Applications

CGI 2017 Editorial (TVCJ)

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