Sparse GPU Voxelization of Yarn‐Level Cloth

López‐Moreno, Jorge; Miraut, David; Cirio, Gabriel; Otaduy, Miguel Á.

doi:10.1111/cgf.12782

Cited by 10 publications

(3 citation statements)

References 32 publications

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“…Many voxelisation approaches have taken the advantage of a GPU to perform fast voxelisations [2,3,26,28,35,39,45,87,88]. Additionally, the use of general purpose graphics processing units (GPGPUs) has become a common tool for high-performance computing, which allows access to many GPUs and parallelisation of many computational tasks [82].…”

Section: Voxel Hardware Technologymentioning

confidence: 99%

Voxelisation Algorithms and Data Structures: A Review

Aleksandrov

Zlatanova

Heslop

2021

Sensors

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Voxel-based data structures, algorithms, frameworks, and interfaces have been used in computer graphics and many other applications for decades. There is a general necessity to seek adequate digital representations, such as voxels, that would secure unified data structures, multi-resolution options, robust validation procedures and flexible algorithms for different 3D tasks. In this review, we evaluate the most common properties and algorithms for voxelisation of 2D and 3D objects. Thus, many voxelisation algorithms and their characteristics are presented targeting points, lines, triangles, surfaces and solids as geometric primitives. For lines, we identify three groups of algorithms, where the first two achieve different voxelisation connectivity, while the third one presents voxelisation of curves. We can say that surface voxelisation is a more desired voxelisation type compared to solid voxelisation, as it can be achieved faster and requires less memory if voxels are stored in a sparse way. At the same time, we evaluate in the paper the available voxel data structures. We split all data structures into static and dynamic grids considering the frequency to update a data structure. Static grids are dominated by SVO-based data structures focusing on memory footprint reduction and attributes preservation, where SVDAG and SSVDAG are the most advanced methods. The state-of-the-art dynamic voxel data structure is NanoVDB which is superior to the rest in terms of speed as well as support for out-of-core processing and data management, which is the key to handling large dynamically changing scenes. Overall, we can say that this is the first review evaluating the available voxelisation algorithms for different geometric primitives as well as voxel data structures.

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Section: Voxel Hardware Technologymentioning

confidence: 99%

Voxelisation Algorithms and Data Structures: A Review

Aleksandrov

Zlatanova

Heslop

2021

Sensors

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“…We smooth the polyline yarns using Catmull-Rom splines, and we then use a modified version of the Lumislice method [Chen et al 2003;Lopez-Moreno et al 2014] to define the volumetric representation of the yarn geometry to be passed to the Mitsuba renderer. Each smoothed yarn is composed of thousands of twisted microfibers, and we set slices representing the microfiber density distribution perpendincularly along the thread segment at regular steps and incremental rotations.…”

Section: Renderingmentioning

confidence: 99%

Yarn-level simulation of woven cloth

Cirio¹,

López‐Moreno²,

Miraut³

et al. 2014

ACM Trans. Graph.

Self Cite

118

110

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Figure 1: Yarn-level simulation of a shirt with 2023 yarns and 350530 crossing nodes. We produce a snag on the shirt by pulling on a seam node. Fine-scale deformations showing yarn sliding and thin wrinkles are combined with large-scale motion of the shirt. AbstractThe large-scale mechanical behavior of woven cloth is determined by the mechanical properties of the yarns, the weave pattern, and frictional contact between yarns. Using standard simulation methods for elastic rod models and yarn-yarn contact handling, the simulation of woven garments at realistic yarn densities is deemed intractable. This paper introduces an efficient solution for simulating woven cloth at the yarn level. Central to our solution is a novel discretization of interlaced yarns based on yarn crossings and yarn sliding, which allows modeling yarn-yarn contact implicitly, avoiding contact handling at yarn crossings altogether. Combined with models for internal yarn forces and inter-yarn frictional contact, as well as a massively parallel solver, we are able to simulate garments with hundreds of thousands of yarn crossings at practical framerates on a desktop machine, showing combinations of large-scale and fine-scale effects induced by yarn-level mechanics.

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“….24. Additional examples of transmittance in high-resolution volumes of locally-correlated media (procedurally generated after[Lopez-Moreno et al 2015]…”

mentioning

confidence: 99%

A radiative transfer framework for spatially-correlated materials

2018

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Fig. 1. Spatial correlation in media leads to non-exponential light transport, which significantly affects appearance. The image shows volumetric renderings of translucent dragons made of materials with the same density per unit differential volume µ = 10 (isotropic, albedo Λ = .8), but different correlations. Left: using classic light transport, where material particles are assumed to be uncorrelated. Middle and right: positive and negative correlation, respectively, using our novel framework for spatially-correlated materials. The insets show illustrative views of scatterer correlation for each dragon.We introduce a non-exponential radiative framework that takes into account the local spatial correlation of scattering particles in a medium. Most previous works in graphics have ignored this, assuming uncorrelated media with a uniform, random local distribution of particles. However, positive and negative correlation lead to slower-and faster-than-exponential attenuation respectively, which cannot be predicted by the Beer-Lambert law. As our results show, this has a major effect on extinction, and thus appearance. From recent advances in neutron transport, we first introduce our Extended Generalized Boltzmann Equation, and develop a general framework for light transport in correlated media. We lift the limitations of the original formulation, including an analysis of the boundary conditions, and present a model suitable for computer graphics, based on optical properties of the media and statistical distributions of scatterers. In addition, we present an analytic expression for transmittance in the case of positive correlation, and show how to incorporate it efficiently into a Monte Carlo renderer. We show results with a wide range of both positive and negative correlation, and demonstrate the differences compared to classic light transport.

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Sparse GPU Voxelization of Yarn‐Level Cloth

Cited by 10 publications

References 32 publications

Voxelisation Algorithms and Data Structures: A Review

Voxelisation Algorithms and Data Structures: A Review

Yarn-level simulation of woven cloth

A radiative transfer framework for spatially-correlated materials

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