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2024-11-04 02:01:58

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Open Graph

title

description

Depth Anything V2

image

site name

author

updated

2026-02-16 05:13:05

raw text

Depth Anything V2 Depth Anything V2 Lihe Yang 1 &nbsp &nbsp &nbsp &nbsp &nbsp &nbsp Bingyi Kang 2† &nbsp &nbsp &nbsp &nbsp &nbsp Zilong Huang 2 &nbsp &nbsp &nbsp &nbsp &nbsp Zhen Zhao &nbsp &nbsp &nbsp &nbsp &nbsp Xiaogang Xu &nbsp &nbsp &nbsp &nbsp &nbsp Jiashi Feng 2 &nbsp &nbsp &nbsp &nbsp &nbsp Hengshuang Zhao 1* 1 HKU &nbsp &nbsp &nbsp &nbsp &nbsp &nbsp &nbsp &nbsp &nbsp &nbsp &nbsp &nbsp &nbsp &nbsp &nbsp 2 TikTok † project lead &nbsp &nbsp &nbsp &nbsp &nbsp &nbsp &nbsp * corresponding author NeurIPS 2024 arXiv Paper Code Demo DA-2K Benchmark Depth Anything V2 is trained from 595K synthetic labeled images and 62M+ real unlabeled images , providing the most capable monocular depth estimation (MDE) model with the following features: more fine-grained details than Depth Anything V1 more robust than Depth Anything V1 and SD-based models ( e.g. , Marigold, Geowizard) more efficient (10x faster) and more lightw...

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