Reconstruction of Implicit Surfaces from Fluid Particles Using Convolutional Neural Networks
No Thumbnail Available
Date
2024
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
In this paper, we present a novel network-based approach for reconstructing signed distance functions from fluid particles. The method uses a weighting kernel to transfer particles to a regular grid, which forms the input to a convolutional neural network. We propose a regression-based regularization to reduce surface noise without penalizing high-curvature features. The reconstruction exhibits improved spatial surface smoothness and temporal coherence compared with existing state of the art surface reconstruction methods. The method is insensitive to particle sampling density and robustly handles thin features, isolated particles, and sharp edges.
Description
CCS Concepts: Computing methodologies->Physical simulation; Point-based models
@article{10.1111:cgf.15181,
journal = {Computer Graphics Forum},
title = {{Reconstruction of Implicit Surfaces from Fluid Particles Using Convolutional Neural Networks}},
author = {Zhao, Chen and Shinar, Tamar and Schroeder, Craig},
year = {2024},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.15181}
}