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.

翻译：本文提出了一种名为F$^{2}$-NeRF（Fast-Free-NeRF）的新型基于网格的神经辐射场，用于新视角合成，该方法支持任意输入相机轨迹，且训练仅需数分钟。现有快速基于网格的NeRF训练框架（如Instant-NGP、Plenoxels、DVGO或TensoRF）主要针对有界场景设计，并依赖空间扭曲处理无界场景。现有两种广泛使用的空间扭曲方法仅适用于前向轨迹或360度物体中心轨迹，无法处理任意轨迹。本文深入研究了空间扭曲处理无界场景的机制，并在分析基础上进一步提出了一种名为透视扭曲（perspective warping）的新型空间扭曲方法，允许在基于网格的NeRF框架中处理任意轨迹。大量实验表明，F$^{2}$-NeRF能够在两个标准数据集以及我们收集的新的自由轨迹数据集上，使用相同的透视扭曲方法渲染出高质量图像。项目页面：https://totoro97.github.io/projects/f2-nerf。