Sinésio Pesco

A stratification approach for modeling two-dimensional cell complexes

Abstract This work presents a stratification approach for modeling two-dimensional cell complexes. It introduces the concept of combinatorial stratification and uses the Handlebody theory for cell complexes so as to propose a new representation called Handle-Cell (HC-Rep). This representation deals not only with objects with different dimensionality but also with non-manifolds models. The HC-Rep scheme includes a data structure and a complete set of operators.

Fast Generation of Pointerless Octree Duals

Geometry processing applications frequently rely on octree structures, since they provide simple and efficient hierarchies for discrete data. However, octrees do not guarantee direct continuous interpolation of this data inside its nodes. This motivates the use of the octrees dual structure, which is one of the simplest continuous hierarchical structures. With the emergence of pointerless representations, with their ability to reduce memory footprint and adapt to parallel architectures, the generation of duals of pointerless octrees becomes a natural challenge. This work proposes strategies for dual generation of static or dynamic pointerless octrees. Experimentally, those methods enjoy the memory reduction of pointerless representations and speed up the execution by several factors compared to the usual recursive generation.

Fast adaptive blue noise on polygonal surfaces

This paper proposes a novel method for the computation of hierarchical Poisson disk samplings on polygonal surfaces. The algorithm generates a pointerless hierarchical structure such that each level is a uniform Poisson disk sampling and a subset of the next level. As the main result, given a dynamically-varying importance sampling function defined over a surface, the hierarchy is capable of generating adaptive samplings with blue noise characteristics, temporal-coherence and real-time computation. Classical algorithms produce hierarchies in tight ratios, which is a serious bottleneck specially for a large number of samples. Instead, our method uses sparse ratios and decreases the adaptation error of the hierarchy through a fast optimization process. Therefore, we save a considerable amount of time (up to 74% in our experiments) while preserving the good blue noise properties. We present applications on Non-Photo Realistic rendering (NPR), more specifically, on surface stippling effects. First, we apply our method by taking illumination to be the importance sampling to shade the surface, and second, we dynamically deform a surface with a predefined stippled texture.

Technical Section: Practical considerations on Marching Cubes 33 topological correctness

Chernyaevs Marching Cubes 33 is one of the first algorithms intended to preserve the topology of the trilinear interpolant. In this work, we address three issues with the Marching Cubes 33 algorithm, two of which are related to its original description and one that is related to its variant. In particular, we solve a problem with the core disambiguation procedure of Marching Cubes 33 that prevents the extraction of topologically correct isosurfaces for the ambiguous configuration 13.5. This work closes an existing gap in the topological correctness of Marching Cubes 33. Furthermore, we make our results reproducible, meaning that examples provided in this work can be easily explored and studied. Finally, as part of the philosophy of reproducibility, we provide a corrected version of the Marching Cubes 33 open-source implementation and access to datasets that can be used to verify the correctness of any available topologically correct isosurface extraction implementation that preserves the topology of the trilinear interpolant.

HANDLEBODY REPRESENTATION FOR SURFACES AND ITS APPLICATIONS TO TERRAIN MODELING

This paper presents a new representation based on the Handlebody theory. Such representation includes a topological data structure and a set of operators which performs cut and paste operations on the surface. Several applications to terrain modeling are also discussed to exemplify its use, including feature attachments to the terrain surface.

GEncode: Geometry-driven compression for general meshes

Performances of actual mesh compression algorithms vary significantly depending on the type of model it encodes. These methods rely on prior assumptions on the mesh to be efficient, such as regular connectivity, simple topology and similarity between its elements. However, these priors are implicit in usual schemes, harming their suitability for specific models. In particular, connectivity‐driven schemes are difficult to generalize to higher dimensions and to handle topological singularities. GEncode is a new single‐rate, geometry‐driven compression scheme where prior knowledge of the mesh is plugged into the coder in an explicit manner. It encodes meshes of arbitrary dimension without topological restrictions, but can incorporate topological properties, such as manifoldness, to improve the compression ratio. Prior knowledge of the geometry is taken as an input of the algorithm, represented by a function of the local geometry. This suits particularly well for scanned and remeshed models, where exact geometric priors are available. Compression results surfaces and volumes are competitive with existing schemes.

GEncode: Geometry-Driven Compression in Arbitrary Dimension and Co-Dimension

Among the mesh compression algorithms, different schemes compress better specific categories of model. In particular, geometry-driven approaches have shown outstanding performances on isosurfaces. It would be expected these algorithm to also encode well meshes reconstructed from the geometry, or optimized by a geometric re-meshing. GEncode is a new single-rate compression scheme that compresses the connectivity of these meshes at almost zero-cost. It improves existing geometry-driven schemes for general meshes on both geometry and connectivity compression. This scheme extends naturally to meshes of arbitrary dimensions in arbitrary ambient space, and deals gracefully with non-manifold meshes. Compression results for surfaces are competitive with existing schemes.

Spider Cursor: a simple versatile interaction tool for data visualization and exploration

We present Spider Cursor, an exploration tool for geometric models that has proven useful in analyzing data coming from various sources. The Spider Cursor is simple to learn and use, and is useful to help extract various types of relationships present in the data. Additionally, it implements a dual presentation mode, in which visual and aural attributes are used to represent information. A cursor in the shape of a spider runs on top of a surface, helping location of information at neighboring points of the geometry. That can be used for data analysis as well as model manipulations (such as cuts and marks). Sound is employed to display complementary and supplementary information as well as to help define orientation. This paper describes the basic concepts of the Spider Cursor, and illustrates its use by giving examples of applications.

Tuning Manifold Harmonics Filters

There are several techniques for automatic music visualization, which are included with virtually any media player. The basic ingredient of those techniques is spectral analysis of the sound, used to automatically generate parameters for procedural image generation. However, only a few music visualizations rely on 3d models. This paper proposes to use spectral mesh processing techniques, namely manifold harmonics, to produce 3d music visualization. The images are generated from 3d models by deforming an initial shape, mapping the sound frequencies to the mesh harmonics. A concise representation of such frequency mapping is proposed to permit for an animated gallery interface with genetic reproduction. Such galleries allow the user to quickly navigate between visual effects. Rendering such animated galleries in real-time is a challenging task, since it requires computing and rendering the deformed shapes at a very high rate. This paper introduces a direct GPU implementation of manifold harmonics filters, which allows to display animated gallery.

A Fast Marching Method for the Area Based Affine Distance

Abstract In a previous paper, it was proved that the area based affine distance of a convex region in the plane satisfies a non-homogeneous Monge-Ampère differential equation. Based on this equation, in this paper we propose a fast marching method for the computation of this distance. The proposed algorithm has a lower computational complexity than the direct method and we have proved its convergence. And since the algorithm allows one to obtain a connection from any point of the region to the boundary by a path of decreasing distance, it offers a dynamic point of view for the area based affine distance.