4 research outputs found
VoroCrust: Voronoi Meshing Without Clipping
Polyhedral meshes are increasingly becoming an attractive option with
particular advantages over traditional meshes for certain applications. What
has been missing is a robust polyhedral meshing algorithm that can handle broad
classes of domains exhibiting arbitrarily curved boundaries and sharp features.
In addition, the power of primal-dual mesh pairs, exemplified by
Voronoi-Delaunay meshes, has been recognized as an important ingredient in
numerous formulations. The VoroCrust algorithm is the first provably-correct
algorithm for conforming polyhedral Voronoi meshing for non-convex and
non-manifold domains with guarantees on the quality of both surface and volume
elements. A robust refinement process estimates a suitable sizing field that
enables the careful placement of Voronoi seeds across the surface circumventing
the need for clipping and avoiding its many drawbacks. The algorithm has the
flexibility of filling the interior by either structured or random samples,
while preserving all sharp features in the output mesh. We demonstrate the
capabilities of the algorithm on a variety of models and compare against
state-of-the-art polyhedral meshing methods based on clipped Voronoi cells
establishing the clear advantage of VoroCrust output.Comment: 18 pages (including appendix), 18 figures. Version without compressed
images available on https://www.dropbox.com/s/qc6sot1gaujundy/VoroCrust.pdf.
Supplemental materials available on
https://www.dropbox.com/s/6p72h1e2ivw6kj3/VoroCrust_supplemental_materials.pd
Deep Point Correlation Design
Designing point patterns with desired properties can require substantial
effort, both in hand-crafting coding and mathematical derivation. Retaining
these properties in multiple dimensions or for a substantial number of points
can be challenging and computationally expensive. Tackling those two issues,
we suggest to automatically generate scalable point patterns from design
goals using deep learning. We phrase pattern generation as a deep composition of weighted distance-based unstructured filters. Deep point pattern
design means to optimize over the space of all such compositions according to
a user-provided point correlation loss, a small program which measures a pattern’s fidelity in respect to its spatial or spectral statistics, linear or non-linear
(e. g., radial) projections, or any arbitrary combination thereof. Our analysis
shows that we can emulate a large set of existing patterns (blue, green, step,
projective, stair, etc.-noise), generalize them to countless new combinations
in a systematic way and leverage existing error estimation formulations to
generate novel point patterns for a user-provided class of integrand functions.
Our point patterns scale favorably to multiple dimensions and numbers of
points: we demonstrate nearly 10 k points in 10-D produced in one second
on one GPU. All the resources (source code and the pre-trained networks)
can be found at https://sampling.mpi-inf.mpg.de/deepsampling.html
Um ambiente para desevonvoimento de algoritmos de amostragem e remoção de ruĂdo
In the context of Monte Carlo rendering, although many sampling and denoising techniques have been proposed in the last few years, the case for which one should be used for a specific scene is still to be made. Moreover, developing a new technique has required selecting a particular rendering system, which makes the technique tightly coupled to the chosen renderer and limits the amount of scenes it can be tested on. In this work, we propose a renderer-agnostic framework for developing and benchmarking sampling and denoising techniques for Monte Carlo rendering. It decouples techniques from rendering systems by hiding the renderer details behind a general API. This improves productivity and allows for direct comparisons among techniques using scenes from different rendering systems. The proposed framework contains two main parts: a software development kit that helps users to develop and and test their techniques locally, and an online system that allows users to submit their techniques and have them automatically benchmarked on our servers. We demonstrate its effectiveness by using our API to instrument four rendering systems and a variety of Monte Carlo denoising techniques — including recent learning-based ones — and performing a benchmark across different rendering systems.No contexto de Monte Carlo rendering, apesar de diversas tĂ©cnicas de amostragem e remoção de ruĂdo tenham sido propostas nos Ăşltimos anos, aportar qual tĂ©cnica deve ser usada para uma cena especĂfica ainda Ă© uma tarefa difĂcil. AlĂ©m disso, desenvolver uma nova tĂ©cnica requer escolher um renderizador em particular, o que torna a tĂ©cnica dependente do renderizador escolhido e limita a quantidade de cenas disponĂveis para testar a tĂ©cnica. Neste trabalho, um framework para desenvolvimento e avaliação de tĂ©cnicas de amostragem e remoção de ruĂdo para Monte Carlo rendering Ă© proposto. Ele permite desacoplar as tĂ©cnicas dos renderizadores por meio de uma API genĂ©rica, promovendo a reprodutibilidade e permitindo comparações entre tĂ©cnicas utilizando-se cenas de diferentes renderizadores. O sistema proposto contĂ©m duas partes principais: um kit de desenvolvimento de software que ajuda os usuários a desenvolver e testar suas tĂ©cnicas localmente, e um sistema online que permite que usuários submetam tĂ©cnicas para que as mesmas sejam automaticamente avaliadas no nosso servidor. Para demonstramos a efetividade do ambiante proposto, modificamos quatro renderizadores e várias tĂ©cnicas de remoção de ruĂdo — incluindo tĂ©cnicas recentes baseadas em aprendizado de máquina — e efetuamos uma avaliação utilizando cenas de diferentes renderizadores