36-Issue 2

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https://diglib.eg.org/handle/10.2312/2631245

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Chamber Recognition in Cave Data Sets
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Schertler, Nico; Buchroithner, Manfred; Gumhold, Stefan; Loic Barthe and Bedrich Benes
Quantitative analysis of cave systems represented as 3D models is becoming more and more important in the field of cave sciences. One open question is the rigorous identification of chambers in a data set, which has a deep impact on subsequent analysis steps such as size calculation. This affects the international recognition of a cave since especially record-holding caves bear significant tourist attraction potential. In the past, chambers have been identified manually, without any clear definition or guidance. While experts agree on core parts of chambers in general, their opinions may differ in more controversial areas. Since this process is heavily subjective, it is not suited for objective quantitative comparison of caves. Therefore, we present a novel fully-automatic curve skeleton-based chamber recognition algorithm that has been derived from requirements from field experts. We state the problem as a binary labeling problem on a curve skeleton and find a solution through energy minimization. A thorough evaluation of our results with the help of expert feedback showed that our algorithm matches real-world requirements very closely and is thus suited as the foundation for any quantitative cave analysis system.
DeepGarment: 3D Garment Shape Estimation from a Single Image
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Danerek, Radek; Dibra, Endri; Öztireli, A. Cengiz; Ziegler, Remo; Gross, Markus; Loic Barthe and Bedrich Benes
3D garment capture is an important component for various applications such as free-view point video, virtual avatars, online shopping, and virtual cloth fitting. Due to the complexity of the deformations, capturing 3D garment shapes requires controlled and specialized setups. A viable alternative is image-based garment capture. Capturing 3D garment shapes from a single image, however, is a challenging problem and the current solutions come with assumptions on the lighting, camera calibration, complexity of human or mannequin poses considered, and more importantly a stable physical state for the garment and the underlying human body. In addition, most of the works require manual interaction and exhibit high run-times. We propose a new technique that overcomes these limitations, making garment shape estimation from an image a practical approach for dynamic garment capture. Starting from synthetic garment shape data generated through physically based simulations from various human bodies in complex poses obtained through Mocap sequences, and rendered under varying camera positions and lighting conditions, our novel method learns a mapping from rendered garment images to the underlying 3D garment model. This is achieved by training Convolutional Neural Networks (CNN-s) to estimate 3D vertex displacements from a template mesh with a specialized loss function. We illustrate that this technique is able to recover the global shape of dynamic 3D garments from a single image under varying factors such as challenging human poses, self occlusions, various camera poses and lighting conditions, at interactive rates. Improvement is shown if more than one view is integrated. Additionally, we show applications of our method to videos.
Design Transformations for Rule-based Procedural Modeling
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Lienhard, Stefan; Lau, Cheryl; Müller, Pascal; Wonka, Peter; Pauly, Mark; Loic Barthe and Bedrich Benes
We introduce design transformations for rule-based procedural models, e.g., for buildings and plants. Given two or more procedural designs, each specified by a grammar, a design transformation combines elements of the existing designs to generate new designs. We introduce two technical components to enable design transformations. First, we extend the concept of discrete rule switching to rule merging, leading to a very large shape space for combining procedural models. Second, we propose an algorithm to jointly derive two or more grammars, called grammar co-derivation. We demonstrate two applications of our work: we show that our framework leads to a larger variety of models than previous work, and we show fine-grained transformation sequences between two procedural models.
Diffusion Diagrams: Voronoi Cells and Centroids from Diffusion
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Herholz, Philipp; Haase, Felix; Alexa, Marc; Loic Barthe and Bedrich Benes
We define Voronoi cells and centroids based on heat diffusion. These heat cells and heat centroids coincide with the common definitions in Euclidean spaces. On curved surfaces they compare favorably with definitions based on geodesics: they are smooth and can be computed in a stable way with a single linear solve. We analyze the numerics of this approach and can show that diffusion diagrams converge quadratically against the smooth case under mesh refinement, which is better than other common discretization of distance measures in curved spaces. By factorizing the system matrix in a preprocess, computing Voronoi diagrams or centroids amounts to just back-substitution. We show how to localize this operation so that the complexity is linear in the size of the cells and not the underlying mesh. We provide several example applications that show how to benefit from this approach.
Enriching Facial Blendshape Rigs with Physical Simulation
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Kozlov, Yeara; Bradley, Derek; Bächer, Moritz; Thomaszewski, Bernhard; Beeler, Thabo; Gross, Markus; Loic Barthe and Bedrich Benes
Oftentimes facial animation is created separately from overall body motion. Since convincing facial animation is challenging enough in itself, artists tend to create and edit the face motion in isolation. Or if the face animation is derived from motion capture, this is typically performed in a mo-cap booth while sitting relatively still. In either case, recombining the isolated face animation with body and head motion is non-trivial and often results in an uncanny result if the body dynamics are not properly reflected on the face (e.g. the bouncing of facial tissue when running). We tackle this problem by introducing a simple and intuitive system that allows to add physics to facial blendshape animation. Unlike previous methods that try to add physics to face rigs, our method preserves the original facial animation as closely as possible. To this end, we present a novel simulation framework that uses the original animation as per-frame rest-poses without adding spurious forces. As a result, in the absence of any external forces or rigid head motion, the facial performance will exactly match the artist-created blendshape animation. In addition we propose the concept of blendmaterials to give artists an intuitive means to account for changing material properties due to muscle activation. This system allows to automatically combine facial animation and head motion such that they are consistent, while preserving the original animation as closely as possible. The system is easy to use and readily integrates with existing animation pipelines.
Fully Spectral Partial Shape Matching
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Litany, Or; Rodolà, Emanuele; Bronstein, Alex M.; Bronstein, Michael M.; Loic Barthe and Bedrich Benes
We propose an efficient procedure for calculating partial dense intrinsic correspondence between deformable shapes performed entirely in the spectral domain. Our technique relies on the recently introduced partial functional maps formalism and on the joint approximate diagonalization (JAD) of the Laplace-Beltrami operators previously introduced for matching non-isometric shapes. We show that a variant of the JAD problem with an appropriately modified coupling term (surprisingly) allows to construct quasi-harmonic bases localized on the latent corresponding parts. This circumvents the need to explicitly compute the unknown parts by means of the cumbersome alternating minimization used in the previous approaches, and allows performing all the calculations in the spectral domain with constant complexity independent of the number of shape vertices. We provide an extensive evaluation of the proposed technique on standard non-rigid correspondence benchmarks and show state-of-the-art performance in various settings, including partiality and the presence of topological noise.
General Point Sampling with Adaptive Density and Correlations
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Roveri, Riccardo; Öztireli, A. Cengiz; Gross, Markus; Loic Barthe and Bedrich Benes
Analyzing and generating sampling patterns are fundamental problems for many applications in computer graphics. Ideally, point patterns should conform to the problem at hand with spatially adaptive density and correlations. Although there exist excellent algorithms that can generate point distributions with spatially adaptive density or anisotropy, the pair-wise correlation model, blue noise being the most common, is assumed to be constant throughout the space. Analogously, by relying on possibly modulated pair-wise difference vectors, the analysis methods are designed to study only such spatially constant correlations. In this paper, we present the first techniques to analyze and synthesize point patterns with adaptive density and correlations. This provides a comprehensive framework for understanding and utilizing general point sampling. Starting from fundamental measures from stochastic point processes, we propose an analysis framework for general distributions, and a novel synthesis algorithm that can generate point distributions with spatio-temporally adaptive density and correlations based on a locally stationary point process model. Our techniques also extend to general metric spaces. We illustrate the utility of the new techniques on the analysis and synthesis of real-world distributions, image reconstruction, spatio-temporal stippling, and geometry sampling.
A GPU-Adapted Structure for Unstructured Grids
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Zayer, Rhaleb; Steinberger, Markus; Seidel, Hans-Peter; Loic Barthe and Bedrich Benes
A key advantage of working with structured grids (e.g., images) is the ability to directly tap into the powerful machinery of linear algebra. This is not much so for unstructured grids where intermediate bookkeeping data structures stand in the way. On modern high performance computing hardware, the conventional wisdom behind these intermediate structures is further challenged by costly memory access, and more importantly by prohibitive memory resources on environments such as graphics hardware. In this paper, we bypass this problem by introducing a sparse matrix representation for unstructured grids which not only reduces the memory storage requirements but also cuts down on the bulk of data movement from global storage to the compute units. In order to take full advantage of the proposed representation, we augment ordinary matrix multiplication by means of action maps, local maps which encode the desired interaction between grid vertices. In this way, geometric computations and topological modifications translate into concise linear algebra operations. In our algorithmic formulation, we capitalize on the nature of sparse matrix-vector multiplication which allows avoiding explicit transpose computation and storage. Furthermore, we develop an efficient vectorization to the demanding assembly process of standard graph and finite element matrices.
Informative Descriptor Preservation via Commutativity for Shape Matching
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Nogneng, Dorian; Ovsjanikov, Maks; Loic Barthe and Bedrich Benes
We consider the problem of non-rigid shape matching, and specifically the functional maps framework that was recently proposed to find correspondences between shapes. A key step in this framework is to formulate descriptor preservation constraints that help to encode the information (e.g., geometric or appearance) that must be preserved by the unknown map. In this paper, we show that considering descriptors as linear operators acting on functions through multiplication, rather than as simple scalar-valued signals, allows to extract significantly more information from a given descriptor and ultimately results in a more accurate functional map estimation. Namely, we show that descriptor preservation constraints can be formulated via commutativity with respect to the unknown map, which can be conveniently encoded by considering relations between matrices in the discrete setting. As a result, when the vector space spanned by the descriptors has a dimension smaller than that of the reduced basis, our optimization may still provide a fully-constrained system leading to accurate point-to-point correspondences, while previous methods might not. We demonstrate on a wide variety of experiments that our approach leads to significant improvement for functional map estimation by helping to reduce the number of necessary descriptor constraints by an order of magnitude, even given an increase in the size of the reduced basis.
Interactive Modeling and Authoring of Climbing Plants
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Hädrich, Torsten; Benes, Bedrich; Deussen, Oliver; Pirk, Sören; Loic Barthe and Bedrich Benes
We present a novel system for the interactive modeling of developmental climbing plants with an emphasis on efficient control and plausible physics response. A plant is represented by a set of connected anisotropic particles that respond to the surrounding environment and to their inner state. Each particle stores biological and physical attributes that drive growth and plant adaptation to the environment such as light sensitivity, wind interaction, and physical obstacles. This representation allows for the efficient modeling of external effects that can be induced at any time without prior analysis of the plant structure. In our framework we exploit this representation to provide powerful editing capabilities that allow to edit a plant with respect to its structure and its environment while maintaining a biologically plausible appearance. Moreover, we couple plants with Lagrangian fluid dynamics and model advanced effects, such as the breaking and bending of branches. The user can thus interactively drag and prune branches or seed new plants in dynamically changing environments. Our system runs in real-time and supports up to 20 plant instances with 25k branches in parallel. The effectiveness of our approach is demonstrated through a number of interactive experiments, including modeling and animation of different species of climbing plants on complex support structures.
kDet: Parallel Constant Time Collision Detection for Polygonal Objects
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Weller, René; Debowski, Nicole; Zachmann, Gabriel; Loic Barthe and Bedrich Benes
We define a novel geometric predicate and a class of objects that enables us to prove a linear bound on the number of intersecting polygon pairs for colliding 3D objects in that class. Our predicate is relevant both in theory and in practice: it is easy to check and it needs to consider only the geometric properties of the individual objects - it does not depend on the configuration of a given pair of objects. In addition, it characterizes a practically relevant class of objects: we checked our predicate on a large database of real-world 3D objects and the results show that it holds for all but the most pathological ones. Our proof is constructive in that it is the basis for a novel collision detection algorithm that realizes this linear complexity also in practice. Additionally, we present a parallelization of this algorithm with a worst-case running time that is independent of the number of polygons. Our algorithm is very well suited not only for rigid but also for deformable and even topology-changing objects, because it does not require any complex data structures or pre-processing. We have implemented our algorithm on the GPU and the results show that it is able to find in real-time all colliding polygons for pairs of deformable objects consisting of more than 200k triangles, including self-collisions.
On Realism of Architectural Procedural Models
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Beneš, Jan; Kelly, Tom; Děchtěrenko, Filip; Křivánek, Jaroslav; Müller, Pascal; Loic Barthe and Bedrich Benes
The goal of procedural modeling is to generate realistic content. The realism of this content is typically assessed by qualitatively evaluating a small number of results, or, less frequently, by conducting a user study. However, there is a lack of systematic treatment and understanding of what is considered realistic, both in procedural modeling and for images in general. We conduct a user study that primarily investigates the realism of procedurally generated buildings. Specifically, we investigate the role of fine and coarse details, and investigate which other factors contribute to the perception of realism. We find that realism is carried on different scales, and identify other factors that contribute to the realism of procedural and non-procedural buildings.
ShapeGenetics: Using Genetic Algorithms for Procedural Modeling
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Haubenwallner, Karl; Seidel, Hans-Peter; Steinberger, Markus; Loic Barthe and Bedrich Benes
In this paper, we show that genetic algorithms (GA) can be used to control the output of procedural modeling algorithms. We propose an efficient way to encode the choices that have to be made during a procedural generation as a hierarchical genome representation. In combination with mutation and reproduction operations specifically designed for controlled procedural modeling, our GA can evolve a population of individual models close to any high-level goal. Possible scenarios include a volume that should be filled by a procedurally grown tree or a painted silhouette that should be followed by the skyline of a procedurally generated city. These goals are easy to set up for an artist compared to the tens of thousands of variables that describe the generated model and are chosen by the GA. Previous approaches for controlled procedural modeling either use Reversible Jump Markov Chain Monte Carlo (RJMCMC) or Stochastically-Ordered Sequential Monte Carlo (SOSMC) as workhorse for the optimization. While RJMCMC converges slowly, requiring multiple hours for the optimization of larger models, it produces high quality models. SOSMC shows faster convergence under tight time constraints for many models, but can get stuck due to choices made in the early stages of optimization. Our GA shows faster convergence than SOSMC and generates better models than RJMCMC in the long run.

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Browsing 36-Issue 2 by Subject "Computational Geometry and Object Modeling"

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