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- ByteDance-Seed Depth-Anything-3 - GitHub
This work presents Depth Anything 3 (DA3), a model that predicts spatially consistent geometry from arbitrary visual inputs, with or without known camera poses In pursuit of minimal modeling, DA3 yields two key insights: 📐 DA3 Metric Series (DA3Metric-Large) A specialized model fine-tuned for
- 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
- [2401. 10891] Depth Anything: Unleashing the Power of Large-Scale . . .
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 3 - a depth-anything Collection - Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science
- Depth Anything V3 Guide 2025 - Use Cases and ComfyUI Integration . . .
Complete Depth Anything V3 guide covering all model variants, practical use cases for robotics and AR VR, plus step-by-step ComfyUI integration for AI creative workflows
- 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 3: Recovering the Visual Space from Any Views
Reconstruction Accuracy 1“Depth Anything 3” marks a new generation for the series, expanding from monocular to any-view inputs, built on our conviction that depth is the cornerstone of understanding the physical world
- 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
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