MOE-INR:隐式神经表示与专家混合用于时间变化的体积数据压缩
IEEE transactions on visualization and computer graphics
|November 21, 2025
概括
专家混合隐式神经表示 (MoE-INR) 通过自动细分字段来改善时间变化的数据压缩. 这种新的方法在表示复杂的时空数据方面优于现有的方法.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 数据压缩数据压缩
背景情况:
- 隐式神经表示 (INR) 对于高维信号建模是有效的.
- 现有的INR方法与复杂的模式和边界文物作斗争.
研究的目的:
- 引入MoE-INR,一种使用混合专家框架的INR架构.
- 为了解决当前INR在建模复杂的时间变化的体积数据方面的局限性.
主要方法:
- 开发了MoE-INR,拥有政策网络,共享编码器和专家解码器.
- 政策网络自动化了领域的细分和专家分配.
- 统一框架可以容纳多种INR类型 (传统,基于网格的,整体).
主要成果:
- 能源部-INR显著超过非能源部和基于能源部的INR.
- 与传统的损耗压缩方法相比,其表现优越.
- 在各种压缩比率中实现了更好的定量和质量指标.
结论:
- MoE-INR为时间变化的体积数据表示和压缩提供了强大的解决方案.
- 专家混合的方法增强了复杂的时空领域的建模.
- MoE-INR代表了隐性神经表示技术的重大进步.
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