Depth Anything V2：抖音开源高性能任何单目图像深度估计V2版本，并开放具有精确注释和多样化场景的多功能评估基准

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题目： Depth Anything V2
作者: Lihe Yang; Bingyi Kang; Zilong Huang; Zhen Zhao; Xiaogang Xu; Jiashi Feng; Hengshuang Zhao
DOI: 10.48550/arXiv.2406.09414
摘要: This work presents Depth Anything V2. Without pursuing fancy techniques, we aim to reveal crucial findings to pave the way towards building a powerful monocular depth estimation model. Notably, compared with V1, this version produces much finer and more robust depth predictions through three key practices: 1) replacing all labeled real images with synthetic images, 2) scaling up the capacity of our teacher model, and 3) teaching student models via the bridge of large-scale pseudo-labeled real images. Compared with the lates