This paper presents a novel grid-based NeRF called F2-NeRF (Fast-Free-NeRF) for novel view synthesis, which enables arbitrary input camera trajectories and only costs a few minutes for training. Existing fast grid-based NeRF training frameworks, like Instant-NGP, Plenoxels, DVGO, or TensoRF, are mainly designed for bounded scenes and rely on space warping to handle unbounded scenes. Existing two widely-used space-warping methods are only designed for the forward-facing trajectory or the 360-degree object-centric trajectory but cannot process arbitrary trajectories. In this paper, we delve deep into the mechanism of space warping to handle unbounded scenes. Based on our analysis, we further propose a novel space-warping method called perspective warping, which allows us to handle arbitrary trajectories in the grid-based NeRF framework. Extensive experiments demonstrate that F2-NeRF is able to use the same perspective warping to render high-quality images on two standard datasets and a new free trajectory dataset collected by us. Project page: https://totoro97.github.io/projects/f2-nerf.

本文介绍了一种用于新视角合成的新型基于网格的NeRF，名为F2-NeRF。它能够处理任意输入相机轨迹，仅需要几分钟的训练时间。本文深入探讨了空间扭曲的机制以处理不受限制的场景，并提出了一种新的基于透视的空间扭曲方法，能够处理基于网格的NeRF中的任意轨迹。广泛的实验表明，F2-NeRF可以使用相同的透视变形在两个标准数据集和我们收集的新的自由轨迹数据集上渲染高质量的图像。

F$^{2}$-NeRF: 基于自由相机轨迹的快速神经辐射场训练