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- Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
This work presents Depth Anything, a highly practical solution for robust monocular depth estimation by training on a combination of 1 5M labeled images and 62M+ unlabeled images Try our latest Depth Anything V2 models!
- Depth Anything
This work presents Depth Anything, a highly practical solution for robust monocular depth estimation Without pursuing novel technical modules, we aim to build a simple yet powerful foundation model dealing with any images under any circumstances
- Depth Anything V2：深度万象 V2 - 知乎
摘要本文介绍了 Depth Anything V2。 在不追求复杂技术的前提下，我们旨在揭示一些关键发现，为构建强大的单目深度估计模型铺平道路。
- depth-anything (Depth Aything) - Hugging Face
This is the organization of Depth Anything, which refers to a series of foundation models built for depth estimation Currently, we have two collections, including Depth-Anything-V1 and Depth-Anything-V2
- Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
This work presents Depth Anything, a highly practical solution for robust monocular depth estimation Without pursuing novel technical modules, we aim to build a simple yet powerful foundation model dealing with any images under any circumstances
- Depth Anything V2 - Hugging Face 文档
Depth Anything 模型，顶部带有一个深度估计头部（由 3 个卷积层组成），例如用于 KITTI、NYUv2。 此模型继承自 PreTrainedModel。 请查阅超类文档，了解库为其所有模型实现的通用方法（例如下载或保存、调整输入嵌入大小、修剪头部等）。
- Depth Anything V2
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
- ComfyUI新节点Depth Anything V3，一键实现3D重建 - 哔哩哔哩
ComfyUI新节点Depth Anything V3，一键实现3D重建 + ControlNet深度控制 ，支持输出glb ply格式模型！
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