AlSub: Fully Parallel and Modular Subdivision

In recent years, mesh subdivision---the process of forging smooth free-form surfaces from coarse polygonal meshes---has become an indispensable production instrument. Although subdivision performance is crucial during simulation, animation and rendering, state-of-the-art approaches still rely on serial implementations for complex parts of the subdivision process. Therefore, they often fail to harness the power of modern parallel devices, like the graphics processing unit (GPU), for large parts of the algorithm and must resort to time-consuming serial preprocessing. In this paper, we show that a complete parallelization of the subdivision process for modern architectures is possible. Building on sparse matrix linear algebra, we show how to structure the complete subdivision process into a sequence of algebra operations. By restructuring and grouping these operations, we adapt the process for different use cases, such as regular subdivision of dynamic meshes, uniform subdivision for immutable topology, and feature-adaptive subdivision for efficient rendering of animated models. As the same machinery is used for all use cases, identical subdivision results are achieved in all parts of the production pipeline. As a second contribution, we show how these linear algebra formulations can effectively be translated into efficient GPU kernels. Applying our strategies to $\sqrt{3}$, Loop and Catmull-Clark subdivision shows significant speedups of our approach compared to state-of-the-art solutions, while we completely avoid serial preprocessing.Comment: Changed structure Added content Improved description

3D Shape Segmentation with Projective Convolutional Networks

This paper introduces a deep architecture for segmenting 3D objects into their labeled semantic parts. Our architecture combines image-based Fully Convolutional Networks (FCNs) and surface-based Conditional Random Fields (CRFs) to yield coherent segmentations of 3D shapes. The image-based FCNs are used for efficient view-based reasoning about 3D object parts. Through a special projection layer, FCN outputs are effectively aggregated across multiple views and scales, then are projected onto the 3D object surfaces. Finally, a surface-based CRF combines the projected outputs with geometric consistency cues to yield coherent segmentations. The whole architecture (multi-view FCNs and CRF) is trained end-to-end. Our approach significantly outperforms the existing state-of-the-art methods in the currently largest segmentation benchmark (ShapeNet). Finally, we demonstrate promising segmentation results on noisy 3D shapes acquired from consumer-grade depth cameras.Comment: This is an updated version of our CVPR 2017 paper. We incorporated new experiments that demonstrate ShapePFCN performance under the case of consistent *upright* orientation and an additional input channel in our rendered images for encoding height from the ground plane (upright axis coordinate values). Performance is improved in this settin

Accelerating and simulating detected physical interations

The aim of this doctoral thesis is to present a body of work aimed at improving performance and developing new methods for animating physical interactions using simulation in virtual environments. To this end we develop a number of novel parallel collision detection and fracture simulation algorithms. Methods for traversing and constructing bounding volume hierarchies (BVH) on graphics processing units (GPU) have had a wide success. In particular, they have been adopted widely in simulators, libraries and benchmarks as they allow applications to reach new heights in terms of performance. Even with such a development however, a thorough adoption of techniques has not occurred in commercial and practical applications. Due to this, parallel collision detection on GPUs remains a relatively niche problem and a wide number of applications could benefit from a significant boost in proclaimed performance gains. In fracture simulations, explicit surface tracking methods have a good track record of success. In particular they have been adopted thoroughly in 3D modelling and animation software like Houdini [124] as they allow accurate simulation of intricate fracture patterns with complex interactions, which are generated using physical laws. Even so, existing methods can pose restrictions on the geometries of simulated objects. Further, they often have tight dependencies on implicit surfaces (e.g. level sets) for representing cracks and performing cutting to produce rigid-body fragments. Due to these restrictions, catering to various geometries can be a challenge and the memory cost of using implicit surfaces can be detrimental and without guarantee on the preservation of sharp features. We present our work in four main chapters. We first tackle the problem in the accelerating collision detection on the GPU via BVH traversal - one of the most demanding components during collision detection. Secondly, we show the construction of a new representation of the BVH called the ostensibly implicit tree - a layout of nodes in memory which is encoded using the bitwise representation of the number of enclosed objects in the tree (e.g. polygons). Thirdly, we shift paradigm to the task of simulating breaking objects after collision: we show how traditional finite elements can be extended as a way to prevent frequent re-meshing during fracture evolution problems. Finally, we show how the fracture surface–represented as an explicit (e.g. triangulated) surface mesh–is used to generate rigid body fragments using a novel approach to mesh cutting

Arbitrary topology meshes in geometric design and vector graphics

Meshes are a powerful means to represent objects and shapes both in 2D and 3D, but the techniques based on meshes can only be used in certain regular settings and restrict their usage. Meshes with an arbitrary topology have many interesting applications in geometric design and (vector) graphics, and can give designers more freedom in designing complex objects. In the first part of the thesis we look at how these meshes can be used in computer aided design to represent objects that consist of multiple regular meshes that are constructed together. Then we extend the B-spline surface technique from the regular setting to work on extraordinary regions in meshes so that multisided B-spline patches are created. In addition, we show how to render multisided objects efficiently, through using the GPU and tessellation. In the second part of the thesis we look at how the gradient mesh vector graphics primitives can be combined with procedural noise functions to create expressive but sparsely defined vector graphic images. We also look at how the gradient mesh can be extended to arbitrary topology variants. Here, we compare existing work with two new formulations of a polygonal gradient mesh. Finally we show how we can turn any image into a vector graphics image in an efficient manner. This vectorisation process automatically extracts important image features and constructs a mesh around it. This automatic pipeline is very efficient and even facilitates interactive image vectorisation

Fractional super-resolution of voxelized point clouds

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2021.Neste trabalho, apresentamos um método para super-resolver nuvens de pontos por um fator fracionário, utilizando um dicionário construído a partir de auto-similaridades presentes na versão subamostrada. Dada a geometria de uma nuvem de pontos subamostrada , juntamente com o correspondente fator de subamostragem , 1 < ≤ 2, o método proposto determina o conjunto de pontos que podem ter gerado e estima quais desses pontos, de fato, existem em (super resolução). Considerando que a geometria de uma nuvem de pontos é aproximadamente auto similar em diferentes escalas de subamostragem, cria-se um dicionário relacionando a configuração de ocupação da vizinhança com a ocupação de nós-filhos. O dicionário é obtido a partir de nova subamostragem da geometria de entrada utilizando o mesmo fator . Desta forma, leva-se em conta as irregularidades da subamostragem por fatores fracionários no desenvolvimento da super-resolução. A textura da nuvem de pontos é interpolada utilizando a média ponderada das cores de vizinhos adjacentes. Diversos conteúdos de diferentes fontes foram testados e resultados interessantes foram obtidos. Adicionalmente, apresentamos uma aplicação direta do método de super-resolução para melhorar a compressão de nuvens de pontos utilizando o codificador G-PCC do MPEG.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).We present a method to super-resolve voxelized point clouds downsampled by a fractional factor, using a lookup-table (LUT) constructed from self-similarities from their own downsampled neighbourhoods. Given a downsampled point cloud geometry , and its corresponding fractional downsampling factor , 1 < ≤ 2, the proposed method determines the set of positions that may have generated , and estimates which of these positions were indeed occupied (super resolution). Assuming that the geometry of a point cloud is approximately self-similar at different scales, a LUT relating downsampled neighbourhood configurations with children occupancy configurations can be estimated by further downsampling the input point cloud, and by taking into account the irregular children distribution derived from fractional downsampling. For completeness, we also interpolate texture by averaging colors from adjacent neighbour voxels. Extensive tests over different datasets are presented, and interesting results were obtained. We further present a direct application to improve point cloud compression using MPEG’s G-PCC codec

Optical flow estimation via steered-L1 norm

Global variational methods for estimating optical flow are among the best performing methods due to the subpixel accuracy and the ‘fill-in’ effect they provide. The fill-in effect allows optical flow displacements to be estimated even in low and untextured areas of the image. The estimation of such displacements are induced by the smoothness term. The L1 norm provides a robust regularisation term for the optical flow energy function with a very good performance for edge-preserving. However this norm suffers from several issues, among these is the isotropic nature of this norm which reduces the fill-in effect and eventually the accuracy of estimation in areas near motion boundaries. In this paper we propose an enhancement to the L1 norm that improves the fill-in effect for this smoothness term. In order to do this we analyse the structure tensor matrix and use its eigenvectors to steer the smoothness term into components that are ‘orthogonal to’ and ‘aligned with’ image structures. This is done in primal-dual formulation. Results show a reduced end-point error and improved accuracy compared to the conventional L1 norm

Optical flow estimation via steered-L1 norm

Global variational methods for estimating optical flow are among the best performing methods due to the subpixel accuracy and the ‘fill-in’ effect they provide. The fill-in effect allows optical flow displacements to be estimated even in low and untextured areas of the image. The estimation of such displacements are induced by the smoothness term. The L1 norm provides a robust regularisation term for the optical flow energy function with a very good performance for edge-preserving. However this norm suffers from several issues, among these is the isotropic nature of this norm which reduces the fill-in effect and eventually the accuracy of estimation in areas near motion boundaries. In this paper we propose an enhancement to the L1 norm that improves the fill-in effect for this smoothness term. In order to do this we analyse the structure tensor matrix and use its eigenvectors to steer the smoothness term into components that are ‘orthogonal to’ and ‘aligned with’ image structures. This is done in primal-dual formulation. Results show a reduced end-point error and improved accuracy compared to the conventional L1 norm