DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision

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 10510 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.