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This paper presents a novel grid-based NeRF called F2-NeRF (Fast-Free-NeRF)for novel view synthesis, which enables arbitrary input camera trajectories andonly costs a few minutes for training. Existing fast grid-based NeRF trainingframeworks, like Instant-NGP, Plenoxels, DVGO, or TensoRF, are mainly designedfor bounded scenes and rely on space warping to handle unbounded scenes.Existing two widely-used space-warping methods are only designed for theforward-facing trajectory or the 360-degree object-centric trajectory butcannot process arbitrary trajectories. In this paper, we delve deep into themechanism 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 NeRFframework. Extensive experiments demonstrate that F2-NeRF is able to use thesame perspective warping to render high-quality images on two standard datasetsand a new free trajectory dataset collected by us. Project page:https://totoro97.github.io/projects/f2-nerf.

F$^{2}$-NeRF: Fast Neural Radiance Field Training with Free Camera Trajectories