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 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/.
658e47afa260709928800ce7
Lu Ling
claimed_verified
2024-01-02T09:28:02.183Z
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Lu Ling
user
LuLing
658e47afa260709928800ce8
Yichen Sheng
658e47afa260709928800ce9
Zhi Tu
658e47afa260709928800cea
Wentian Zhao
658e47afa260709928800ceb
Cheng Xin
658e47afa260709928800cec
Kun Wan
claimed_verified
2025-04-15T08:04:58.030Z
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Kun Wan
user
timecuriosity
658e47afa260709928800ced
Lantao Yu
658e47afa260709928800cee
Qianyu Guo
658e47afa260709928800cef
Zixun Yu
658e47afa260709928800cf0
Yawen Lu
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Xuanmao Li
658e47afa260709928800cf2
Xingpeng Sun
658e47afa260709928800cf3
Rohan Ashok
658e47afa260709928800cf4
Aniruddha Mukherjee
658e47afa260709928800cf5
Hao Kang
658e47afa260709928800cf6
Xiangrui Kong
658e47afa260709928800cf7
Gang Hua
658e47afa260709928800cf8
Tianti Zhang
658e47afa260709928800cf9
Bedrich Benes
658e47afa260709928800cfa
Aniket Bera
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 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/.
A large-scale dataset, DL3DV-10K, featuring diverse real-world 3D scenes enhances benchmarks for novel view synthesis and neural radiance field learning.
neural radiance field novel view synthesis DL3DV-10K 3D representation learning large-scale dataset 3D vision point-of-interest bounded scenes unbounded scenes reflection transparency lighting generalizable NeRF foundation model