CoupNeRF: Property-aware Neural Radiance Fields for Multi-Material Coupled Scenario Reconstruction
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
Neural Radiance Fields (NeRFs) have achieved significant recognition for their proficiency in scene reconstruction and rendering by utilizing neural networks to depict intricate volumetric environments. Despite considerable research dedicated to reconstructing physical scenes, rare works succeed in challenging scenarios involving dynamic, multi-material objects. To alleviate, we introduce CoupNeRF, an efficient neural network architecture that is aware of multiple material properties. This architecture combines physically grounded continuum mechanics with NeRF, facilitating the identification of motion systems across a wide range of physical coupling scenarios. We first reconstruct specific-material of objects within 3D physical fields to learn material parameters. Then, we develop a method to model the neighbouring particles, enhancing the learning process specifically in regions where material transitions occur. The effectiveness of CoupNeRF is demonstrated through extensive experiments, showcasing its proficiency in accurately coupling and identifying the behavior of complex physical scenes that span multiple physics domains.
Description
CCS Concepts: Computing methodologies → Physical simulation; Rendering; Reconstruction
@article{10.1111:cgf.15208,
journal = {Computer Graphics Forum},
title = {{CoupNeRF: Property-aware Neural Radiance Fields for Multi-Material Coupled Scenario Reconstruction}},
author = {Li, Jin and Gao, Yang and Song, Wenfeng and Li, Yacong and Li, Shuai and Hao, Aimin and Qin, Hong},
year = {2024},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.15208}
}