Libor Váša

Bio: Libor Váša is an academic researcher from University of West Bohemia. The author has contributed to research in topics: Polygon mesh & Data compression. The author has an hindex of 12, co-authored 40 publications receiving 583 citations. Previous affiliations of Libor Váša include Information Technology University & Chemnitz University of Technology.

Perceptual metrics for static and dynamic triangle meshes

Abstract: Almost all mesh processing procedures cause some more or less visible changes in the appearance of objects represented by polygonal meshes In many cases, such as mesh watermarking, simplification or lossy compression, the objective is to make the change in appearance negligible, or as small as possible, given some other constraints Measuring the amount of distortion requires taking into account the final purpose of the data In many applications, the final consumer of the data is a human observer, and therefore the perceptibility of the introduced appearance change by a human observer should be the criterion that is taken into account when designing and configuring the processing algorithms In this review, we discuss the existing comparison metrics for static and dynamic (animated) triangle meshes We describe the concepts used in perception-oriented metrics used for 2D image comparison, and we show how these concepts are employed in existing 3D mesh metrics We describe the character of subjective data used for evaluation of mesh metrics and provide comparison results identifying the advantages and drawbacks of each method Finally, we also discuss employing the perception-correlated metrics in perception-oriented mesh processing algorithms

Dihedral Angle Mesh Error: a fast perception correlated distortion measure for fixed connectivity triangle meshes

Abstract: In computer graphics, triangle meshes are ubiquitous as a representation of surface models. Processing of this kind of data, such as compression or watermarking, often involves an unwanted distortion of the surface geometry. Advanced processing algorithms are continuously being proposed, aiming at improving performance (compression ratio, watermark robustness and capacity), while minimizing the introduced distortion. In most cases, the final resulting mesh is intended to be viewed by a human observer, and it is therefore necessary to minimise the amount of distortion perceived by the human visual system. However, only recently there have been studies published on subjective experiments in this field, showing that previously used objective error measures exhibit rather poor correlation with the results of subjective experiments. In this paper, we present results of our own large subjective testing aimed at human perception of triangle mesh distortion. We provide an independent confirmation of the previous result by Lavoue et al. that most current metrics perform poorly, with the exception of the MSDM/MSDM2 metrics. We propose a novel metric based on measuring the distortion of dihedral angles, which provides even higher correlation with the results of our experiments and experiments performed by other researchers. Our metric is about two orders of magnitude faster than MSDM/MSDM2, which makes it much more suitable for usage in iterative optimisation algorithms. © 2012 Wiley Periodicals, Inc.

A Perception Correlated Comparison Method for Dynamic Meshes

Abstract: There are multiple areas of computer graphics where triangular meshes are being altered in order to reduce their size or complexity, while attempting to preserve the original shape of the mesh as closely as possible. Recently, this area of research has been extended to cover even a dynamic case, i.e., surface animations which are compressed and simplified. However, to date very little effort has been made to develop methods for evaluating the results, namely the amount of distortion introduced by the processing. Even the most sophisticated compression methods use distortion evaluation by some kind of mean squared error while the actual relevance of such measure has not been verified so far. In this paper, we point out some serious drawbacks of the existing error measures. We present results of the subjective testing that we have performed, and we derive a new measure called Spatiotemporal edge difference (STED) which is shown to provide much better correlation with subjective opinions on mesh distortion.

CODDYAC: Connectivity Driven Dynamic Mesh Compression

Abstract: Compression of 3D mesh animations is a topic that has received increased attention in recent years, due to increasing capabilities of modern processing and displaying hardware. In this paper we present an improved approach based on known techniques, such as principal component analysis (PCA) and EdgeBreaker, which allows efficient encoding of highly detailed dynamic meshes, exploiting both spatial and temporal coherence. We present the results of our method compared with similar approaches described in literature, showing that using our approach we can achieve better performance in terms of rate/distortion ratio.

On the Efficiency of Image Metrics for Evaluating the Visual Quality of 3D Models

Abstract: 3D meshes are deployed in a wide range of application processes (e.g., transmission, compression, simplification, watermarking and so on) which inevitably introduce geometric distortions that may alter the visual quality of the rendered data. Hence, efficient model-based perceptual metrics, operating on the geometry of the meshes being compared, have been recently introduced to control and predict these visual artifacts. However, since the 3D models are ultimately visualized on 2D screens, it seems legitimate to use images of the models (i.e., snapshots from different viewpoints) to evaluate their visual fidelity. In this work we investigate the use of image metrics to assess the visual quality of 3D models. For this goal, we conduct a wide-ranging study involving several 2D metrics, rendering algorithms, lighting conditions and pooling algorithms, as well as several mean opinion score databases. The collected data allow (1) to determine the best set of parameters to use for this image-based quality assessment approach and (2) to compare this approach to the best performing model-based metrics and determine for which use-case they are respectively adapted. We conclude by exploring several applications that illustrate the benefits of image-based quality assessment.

High-quality streamable free-viewpoint video

Abstract: We present the first end-to-end solution to create high-quality free-viewpoint video encoded as a compact data stream. Our system records performances using a dense set of RGB and IR video cameras, generates dynamic textured surfaces, and compresses these to a streamable 3D video format. Four technical advances contribute to high fidelity and robustness: multimodal multi-view stereo fusing RGB, IR, and silhouette information; adaptive meshing guided by automatic detection of perceptually salient areas; mesh tracking to create temporally coherent subsequences; and encoding of tracked textured meshes as an MPEG video stream. Quantitative experiments demonstrate geometric accuracy, texture fidelity, and encoding efficiency. We release several datasets with calibrated inputs and processed results to foster future research.

Color Appearance Models

Abstract: Color science is a multidisciplinary field with broad applications in industries such as digital imaging, coatings and textiles, food, lighting, archiving, art, and fashion. Accurate definition and measurement of color appearance is a challenging task that directly affects color reproduction in such applications. Color Appearance Models addresses those challenges and offers insight into the preferred solutions. Extensive research on the human visual system (HVS) and color vision has been performed in the last century, and this book contains a good overview of the most important and relevant literature regarding color appearance models.

Coding Algorithms for 3DTV—A Survey

Abstract: Research efforts on 3DTV technology have been strengthened worldwide recently, covering the whole media processing chain from capture to display. Different 3DTV systems rely on different 3D scene representations that integrate various types of data. Efficient coding of these data is crucial for the success of 3DTV. Compression of pixel-type data including stereo video, multiview video, and associated depth or disparity maps extends available principles of classical video coding. Powerful algorithms and open international standards for multiview video coding and coding of video plus depth data are available and under development, which will provide the basis for introduction of various 3DTV systems and services in the near future. Compression of 3D mesh models has also reached a high level of maturity. For static geometry, a variety of powerful algorithms are available to efficiently compress vertices and connectivity. Compression of dynamic 3D geometry is currently a more active field of research. Temporal prediction is an important mechanism to remove redundancy from animated 3D mesh sequences. Error resilience is important for transmission of data over error prone channels, and multiple description coding (MDC) is a suitable way to protect data. MDC of still images and 2D video has already been widely studied, whereas multiview video and 3D meshes have been addressed only recently. Intellectual property protection of 3D data by watermarking is a pioneering research area as well. The 3D watermarking methods in the literature are classified into three groups, considering the dimensions of the main components of scene representations and the resulting components after applying the algorithm. In general, 3DTV coding technology is maturating. Systems and services may enter the market in the near future. However, the research area is relatively young compared to coding of other types of media. Therefore, there is still a lot of room for improvement and new development of algorithms.

Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes

Abstract: Neural signed distance functions (SDFs) are emerging as an effective representation for 3D shapes. State-of-the-art methods typically encode the SDF with a large, fixed-size neural network to approximate complex shapes with implicit surfaces. Rendering with these large networks is, however, computationally expensive since it requires many forward passes through the network for every pixel, making these representations impractical for real-time graphics. We introduce an efficient neural representation that, for the first time, enables real-time rendering of high-fidelity neural SDFs, while achieving state-of-the-art geometry reconstruction quality. We represent implicit surfaces using an octree-based feature volume which adaptively fits shapes with multiple discrete levels of detail (LODs), and enables continuous LOD with SDF interpolation. We further develop an efficient algorithm to directly render our novel neural SDF representation in real-time by querying only the necessary LODs with sparse octree traversal. We show that our representation is 2–3 orders of magnitude more efficient in terms of rendering speed compared to previous works. Furthermore, it produces state-of-the-art reconstruction quality for complex shapes under both 3D geometric and 2D image-space metrics.

Compression of 3D Point Clouds Using a Region-Adaptive Hierarchical Transform

Abstract: In free-viewpoint video, there is a recent trend to represent scene objects as solids rather than using multiple depth maps. Point clouds have been used in computer graphics for a long time, and with the recent possibility of real-time capturing and rendering, point clouds have been favored over meshes in order to save computation. Each point in the cloud is associated with its 3D position and its color. We devise a method to compress the colors in point clouds, which is based on a hierarchical transform and arithmetic coding. The transform is a hierarchical sub-band transform that resembles an adaptive variation of a Haar wavelet. The arithmetic encoding of the coefficients assumes Laplace distributions, one per sub-band. The Laplace parameter for each distribution is transmitted to the decoder using a custom method. The geometry of the point cloud is encoded using the well-established octtree scanning. Results show that the proposed solution performs comparably with the current state-of-the-art, in many occasions outperforming it, while being much more computationally efficient. We believe this paper represents the state of the art in intra-frame compression of point clouds for real-time 3D video.