用高斯-拉盖尔积分加速神经辐射场渲染，无需额外模型或结构。
GL-NeRF: Gauss-Laguerre Quadrature Enables Training-Free NeRF Acceleration
- 采用高斯-拉盖尔积分法重构体积渲染，减少采样点的MLP调用次数。
- 在保持性能微降前提下，显著降低渲染所需MLP调用次数。
- 即插即用，可无缝集成到任意NeRF模型中，适合追求推理速度者。
神经辐射场中的体素渲染因每条射线需大量MLP调用而效率低下。现有方法通过引入新神经网络或数据结构来缓解该问题。本文提出GL-NeRF，从高斯-拉盖尔积分的新视角计算体素渲染，显著减少渲染所需的MLP调用次数，且不引入额外数据结构或神经网络。其简洁形式使该方法可轻松嵌入任意NeRF模型。我们首先论证了高斯-拉盖尔积分的合理性，并在两种不同NeRF模型上验证其即插即用特性。实验表明，在性能损失极小的前提下，GL-NeRF能大幅降低MLP调用次数，具备加速各类NeRF模型的巨大潜力。
原文摘要 · Abstract (English)
Volume rendering in neural radiance fields is inherently time-consuming due to the large number of MLP calls on the points sampled per ray. Previous works would address this issue by introducing new neural networks or data structures. In this work, We propose GL-NeRF, a new perspective of computing volume rendering with the Gauss-Laguerre quadrature. GL-NeRF significantly reduces the number of MLP calls needed for volume rendering, introducing no additional data structures or neural networks. The simple formulation makes adopting GL-NeRF in any NeRF model possible. In the paper, we first justify the use of the Gauss-Laguerre quadrature and then demonstrate this plug-and-play attribute by implementing it in two different NeRF models. We show that with a minimal drop in performance, GL-NeRF can significantly reduce the number of MLP calls, showing the potential to speed up any NeRF model.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。