title: DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision
summary: 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/.
paper_authors._id: 658e47afa260709928800ce7
paper_authors.name: Lu Ling
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-01-02T09:28:02.183Z
paper_authors._id: 6347935d79e42766e212fa53
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6347935d79e42766e212fa53/9BBADaR0yV1wjC91pGYmZ.png
paper_authors.fullname: Lu Ling
paper_authors.type: user
paper_authors.user: LuLing
paper_authors._id: 658e47afa260709928800ce8
paper_authors.name: Yichen Sheng
paper_authors._id: 658e47afa260709928800ce9
paper_authors.name: Zhi Tu
paper_authors._id: 658e47afa260709928800cea
paper_authors.name: Wentian Zhao
paper_authors._id: 658e47afa260709928800ceb
paper_authors.name: Cheng Xin
paper_authors._id: 658e47afa260709928800cec
paper_authors.name: Kun Wan
paper_authors._id: 658e47afa260709928800ced
paper_authors.name: Lantao Yu
paper_authors._id: 658e47afa260709928800cee
paper_authors.name: Qianyu Guo
paper_authors._id: 658e47afa260709928800cef
paper_authors.name: Zixun Yu
paper_authors._id: 658e47afa260709928800cf0
paper_authors.name: Yawen Lu
paper_authors._id: 658e47afa260709928800cf1
paper_authors.name: Xuanmao Li
paper_authors._id: 658e47afa260709928800cf2
paper_authors.name: Xingpeng Sun
paper_authors._id: 658e47afa260709928800cf3
paper_authors.name: Rohan Ashok
paper_authors._id: 658e47afa260709928800cf4
paper_authors.name: Aniruddha Mukherjee
paper_authors._id: 658e47afa260709928800cf5
paper_authors.name: Hao Kang
paper_authors._id: 658e47afa260709928800cf6
paper_authors.name: Xiangrui Kong
paper_authors._id: 658e47afa260709928800cf7
paper_authors.name: Gang Hua
paper_authors._id: 658e47afa260709928800cf8
paper_authors.name: Tianti Zhang
paper_authors._id: 658e47afa260709928800cf9
paper_authors.name: Bedrich Benes
paper_authors._id: 658e47afa260709928800cfa
paper_authors.name: Aniket Bera
paper_title: DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision
paper_summary: 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/.
paper_ai_keywords: neural radiance field (NeRF) 3D vision novel view synthesis (NVS) 3D representation learning scene-level datasets point-of-interest (POI) locations bounded and unbounded scenes reflection transparency lighting generalizable NeRF foundation model for learning 3D representation