  We have witnessed significant progress in deep learning-based 3D vision,
ranging from neural radiance field (NeRF) based 3D representation learning to
applications in novel view synthesis (NVS). However, existing scene-level
datasets for deep learning-based 3D vision, limited to either synthetic
environments or a narrow selection of real-world scenes, are quite
insufficient. This insufficiency not only hinders a comprehensive benchmark of
existing methods but also caps what could be explored in deep learning-based 3D
analysis. To address this critical gap, we present DL3DV-10K, a large-scale
scene dataset, featuring 51.2 million frames from 10,510 videos captured from
65 types of point-of-interest (POI) locations, covering both bounded and
unbounded scenes, with different levels of reflection, transparency, and
lighting. We conducted a comprehensive benchmark of recent NVS methods on
DL3DV-10K, which revealed valuable insights for future research in NVS. In
addition, we have obtained encouraging results in a pilot study to learn
generalizable NeRF from DL3DV-10K, which manifests the necessity of a
large-scale scene-level dataset to forge a path toward a foundation model for
learning 3D representation. Our DL3DV-10K dataset, benchmark results, and
models will be publicly accessible at https://dl3dv-10k.github.io/DL3DV-10K/.
