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

Lu Ling · Yichen Sheng · Zhi Tu · Wentian Zhao · Cheng Xin · Kun Wan · Lantao Yu · Qianyu Guo · Zixun Yu · Yawen Lu · Xuanmao Li · Xingpeng Sun · Rohan Ashok · Aniruddha Mukherjee · Hao Kang · Xiangrui Kong · Gang Hua · Tianyi Zhang · Bedrich Benes · Aniket Bera

Arch 4A-E Poster #251
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Fri 21 Jun 10:30 a.m. PDT — noon PDT


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.

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