Discrete indicator functions on particle-based fluids

Detalhes bibliográficos
Ano de defesa: 2024
Autor(a) principal: Quispe, Filomen Incahuanaco
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Tese
Tipo de acesso: Acesso aberto
Idioma: eng
Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-10012025-154434/
Resumo: While particle-based methods offer compelling advantages for fluid animation due to their versatility, efficiently reconstructing realistic liquid surfaces remains a significant challenge, especially when dealing with a large number of particles. This dissertation addresses this challenge by introducing two novel and efficient surface reconstruction methods specifically designed for particle-based fluid animation. These methods leverage Discrete Indicator Functions (DIFs) to achieve significant improvements. The first approach offers a fast approximation of the liquid surface using a DIF based on particle counts within grid cells. The second approach utilizes the particle distribution within cells to generate a high-quality liquid surface, crucial for visually captivating fluid animations. These DIF-based methods excel in terms of speed, ease of implementation, and adaptability. They seamlessly integrate with existing particle-based fluid solvers and can be implemented on GPUs for further performance gains. The effectiveness of the proposed approaches is rigorously evaluated through experiments against prior surface reconstruction methods. These experiments demonstrate significant improvements in both efficiency and accuracy. This research contributes to the field of fluid animation by providing practical and scalable solutions for creating realistic and visually compelling liquid surfaces.