大规模神经辐射场的场景专家学习异质混合
IEEE transactions on pattern analysis and machine intelligence
|August 27, 2025
概括
交换机NeRF++引入了一个可扩展的神经辐射场 (NeRF) 的新框架. 这种方法有效地处理场景分解和异质性, 实现最先进的染精度与显著的加速度.
科学领域:
- 计算机视觉
- 计算机图形
- 机器学习
背景情况:
- 神经辐射场 (NeRF) 显示出大规模场景建模的前景.
- 现有方法在可学习的分解,场景异质性和效率方面面临挑战.
- 在现实世界应用中,NeRF的可扩展性至关重要.
研究的目的:
- 推出Switch-NeRF++,一个用于大型场景中的可扩展NeRF的统一框架.
- 解决可学习分解,场景异质性和建模效率的局限性.
- 实现高质量和高效的大规模场景建模.
主要方法:
- 开发了一个异质混合的哈希斯专家 (HMoHE) 网络.
- 实现基于哈希的网关网络进行场景分解和点分配.
- 使用不同分辨率哈希格的不同异质哈希专家.
- 通过稀疏的专家组合 (MoE) 框架共同优化门户网络和专家.
主要成果:
- 在大型数据集上实现最先进的场景染精度.
- 显著提高了效率:比Switch-NeRF提高了8倍的训练和16倍的染速度.
- 已成功扩展到各种大型场景,包括超过6.5平方公里的新数据集.
- 验证了可学习异质分解和表示的有效性.
结论:
- 交换机NeRF++为大规模场景提供端到端,高度可扩展的NeRF解决方案.
- 这一框架有效地模拟了场景的多样性,并提高了效率.
- 与现有方法相比,该方法实现了更高的质量和性能.
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