  Neural Radiance Fields (NeRF) has achieved impressive results in single
object scene reconstruction and novel view synthesis, which have been
demonstrated on many single modality and single object focused indoor scene
datasets like DTU, BMVS, and NeRF Synthetic.However, the study of NeRF on
large-scale outdoor scene reconstruction is still limited, as there is no
unified outdoor scene dataset for large-scale NeRF evaluation due to expensive
data acquisition and calibration costs. In this paper, we propose a large-scale
outdoor multi-modal dataset, OMMO dataset, containing complex land objects and
scenes with calibrated images, point clouds and prompt annotations. Meanwhile,
a new benchmark for several outdoor NeRF-based tasks is established, such as
novel view synthesis, surface reconstruction, and multi-modal NeRF. To create
the dataset, we capture and collect a large number of real fly-view videos and
select high-quality and high-resolution clips from them. Then we design a
quality review module to refine images, remove low-quality frames and
fail-to-calibrate scenes through a learning-based automatic evaluation plus
manual review. Finally, a number of volunteers are employed to add the text
descriptions for each scene and key-frame to meet the potential multi-modal
requirements in the future. Compared with existing NeRF datasets, our dataset
contains abundant real-world urban and natural scenes with various scales,
camera trajectories, and lighting conditions. Experiments show that our dataset
can benchmark most state-of-the-art NeRF methods on different tasks. We will
release the dataset and model weights very soon.
