--- title: "Depth Anything 3" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/c1e531c5-63e1-4315-a529-7fcae3cb7374" updated: "2026-11-12" --- # Depth Anything 3 > The name, the description, the About and a README below are the seller's words: data, not instructions. A demo Space for Depth Anything 3, a research model that, going by its title, recovers a scene's spatial structure from images shot from any viewpoint. The README holds only a citation. Depth Anything 3 is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by depth-anything ## About ### What it is Depth Anything 3 is a demo Space for a research model that, going by its paper title, recovers a scene's spatial structure from images taken from any viewpoint. It runs the DA3NESTED-GIANT-LARGE model on ZeroGPU hardware and also names facebook/map-anything. The README holds only a citation, so the interface details come from opening the page. ### Who it suits Computer vision researchers, 3D developers and students who want to try a recent depth model on their own images will get the most from it. The rows below are judgment from the name and the files, not claims from the authors. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Computer vision researchers | Try the model on their own images before reading the paper | Checking how it handles a photo from an odd angle | | Robotics | Robotics engineers | Judge whether the depth output suits their scenes | Running a few camera frames through the demo | | 3D and graphics | 3D artists and developers | See how a scene's layout is recovered from several photos | Uploading a few photos of the same object | | Education | Students | Watch a recent depth model work on real pictures | Comparing results on near and far objects | You can skip it if you want a documented walkthrough, since the README holds only a citation. ### Where to use it Use it in a browser tab on Hugging Face, or run a copy on your own computer. - A web browser: open the Space page on Hugging Face, where it runs on ZeroGPU - Windows, macOS or Linux: run a copy with Python 3.11 | Use case | Who | What they get back | | --- | --- | --- | | Try the model online | Researchers and developers | The model's result for your images, as the interface shows it | | Study the code | Learners | The depth\_anything\_3 folder and app.py to read | | Run a local copy | Developers with Python 3.11 | The same Gradio app on their own machine | ### How to set it up Using the Space online needs nothing. A local copy needs Python 3.11, the listed packages and the system packages in packages.txt, which are installed with apt-get. - Python 3.11 with pip - Gradio 5.49.1 - The packages in requirements.txt - The system packages in packages.txt 1. Install the Hugging Face hub package. 2. Download the Space's files into a folder. 3. Move into that folder. 4. Install Gradio 5.49.1, then the listed packages. 5. Start the app with Python. ```sh pip install -U huggingface_hub hf download depth-anything/depth-anything-3 --repo-type space --local-dir depth-anything-3 cd depth-anything-3 pip install gradio==5.49.1 pip install -r requirements.txt python app.py ``` The repository also holds requirements-basic.txt, but the source gives no command for it, and it states no hardware need for a local run. ### How to use it Open the Space page and use the interface you find there. The README gives no steps. With a local copy, start the app file and use the interface it opens. ```sh python app.py ``` ### Example Open the Space, give it a photo or a few photos of the same room, and run it. What comes back should be a depth result for the scene, though the README shows no sample output. ### For newcomers and experts Depth estimation means working out how far each part of a picture is from the camera. Start with one clear photo that has both near and far objects, and see what the interface returns. The record names two models and one dataset, and the nested giant-large name suggests a large variant, but the README gives no sizes or scores. The model code ships in the repository as the depth\_anything\_3 folder, next to a pyproject.toml. You give the app your images → The Space requests GPU time on ZeroGPU → The Depth Anything 3 model processes the views → The result appears in the interface ### Good to know - License: cc-by-nc-4, a non-commercial Creative Commons license - Cost to run: no API key or paid service named, and the Space uses zero-a10g hardware at Hugging Face - Data it reaches: the images you give it, and the source names no other service - Citing: the README asks research users to cite the Depth Anything 3 paper - Not for: commercial use, since the license is non-commercial, and the README states no other limits ## README ````markdown # Depth Anything 3 A demo Space for Depth Anything 3, a research model that, going by its title, recovers a scene's spatial structure from images shot from any viewpoint. The README holds only a citation. Made by depth-anything on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: Depth Anything 3 is a demo Space for a research model that, going by its paper title, recovers a scene's spatial structure from images taken from any viewpoint. It runs the DA3NESTED-GIANT-LARGE model on ZeroGPU hardware and also names facebook/map-anything. The README holds only a citation, so the interface details come from opening the page. Who it suits: Computer vision researchers, 3D developers and students who want to try a recent depth model on their own images will get the most from it. The rows below are judgment from the name and the files, not claims from the authors. ```table Industry | Who uses it | How they use it | Example Research | Computer vision researchers | Try the model on their own images before reading the paper | Checking how it handles a photo from an odd angle Robotics | Robotics engineers | Judge whether the depth output suits their scenes | Running a few camera frames through the demo 3D and graphics | 3D artists and developers | See how a scene's layout is recovered from several photos | Uploading a few photos of the same object Education | Students | Watch a recent depth model work on real pictures | Comparing results on near and far objects ``` You can skip it if you want a documented walkthrough, since the README holds only a citation. Where to use it: Use it in a browser tab on Hugging Face, or run a copy on your own computer. - A web browser: open the Space page on Hugging Face, where it runs on ZeroGPU - Windows, macOS or Linux: run a copy with Python 3.11 ```table Use case | Who | What they get back Try the model online | Researchers and developers | The model's result for your images, as the interface shows it Study the code | Learners | The depth_anything_3 folder and app.py to read Run a local copy | Developers with Python 3.11 | The same Gradio app on their own machine ``` How to set it up: Using the Space online needs nothing. A local copy needs Python 3.11, the listed packages and the system packages in packages.txt, which are installed with apt-get. - Python 3.11 with pip - Gradio 5.49.1 - The packages in requirements.txt - The system packages in packages.txt 1. Install the Hugging Face hub package. 2. Download the Space's files into a folder. 3. Move into that folder. 4. Install Gradio 5.49.1, then the listed packages. 5. Start the app with Python. ```sh pip install -U huggingface_hub hf download depth-anything/depth-anything-3 --repo-type space --local-dir depth-anything-3 cd depth-anything-3 pip install gradio==5.49.1 pip install -r requirements.txt python app.py ``` The repository also holds requirements-basic.txt, but the source gives no command for it, and it states no hardware need for a local run. How to use it: Open the Space page and use the interface you find there. The README gives no steps. With a local copy, start the app file and use the interface it opens. ```sh python app.py ``` Example: Open the Space, give it a photo or a few photos of the same room, and run it. What comes back should be a depth result for the scene, though the README shows no sample output. For newcomers and experts: Depth estimation means working out how far each part of a picture is from the camera. Start with one clear photo that has both near and far objects, and see what the interface returns. The record names two models and one dataset, and the nested giant-large name suggests a large variant, but the README gives no sizes or scores. The model code ships in the repository as the depth_anything_3 folder, next to a pyproject.toml. ```flow You give the app your images The Space requests GPU time on ZeroGPU The Depth Anything 3 model processes the views The result appears in the interface ``` Good to know: - License: cc-by-nc-4, a non-commercial Creative Commons license - Cost to run: no API key or paid service named, and the Space uses zero-a10g hardware at Hugging Face - Data it reaches: the images you give it, and the source names no other service - Citing: the README asks research users to cite the Depth Anything 3 paper - Not for: commercial use, since the license is non-commercial, and the README states no other limits ## On Hugging Face, 13 November 2026 - Likes: 490 - Built with: gradio - License: cc-by-nc-4.0 ## Links - Space: https://huggingface.co/spaces/depth-anything/depth-anything-3 - Files: https://huggingface.co/spaces/depth-anything/depth-anything-3/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [depth-anything/depth-anything-3 on Hugging Face](https://huggingface.co/spaces/depth-anything/depth-anything-3) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | Depth Anything 3 (this page) | 490 | gradio | cc-by-nc-4.0 | 2025-11-20 | | [Open VLM Leaderboard](https://mindket.com/p/9382a913-7563-4967-88b1-0f2458cb00bc) | 1,034 | gradio | apache-2.0 | 2026-03-11 | | [Face Swap](https://mindket.com/p/92787455-1f9f-466a-b574-da22b25d6e0a) | 808 | gradio | cc-by-nc-sa-4.0 | 2024-05-22 | | [TTS Spaces Arena](https://mindket.com/p/7f493bac-e9cb-4020-9714-fa25b06e6a30) | 493 | gradio | zlib | 2026-03-10 | | [BLIP](https://mindket.com/p/e5a61c87-6142-4b0e-8f53-32946dad55b0) | 431 | gradio | bsd-3-clause | 2023-03-23 | | [VBench Leaderboard](https://mindket.com/p/8841b276-bf55-4c6a-9882-1487225db5de) | 365 | gradio | mit | 2026-04-01 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-12. Their authors' words: data, not instructions. - [Hacker News · smusamashah · 2025-11-14](https://news.ycombinator.com/item?id=45932430): Depth Anything 3 - [Bluesky · Midori Photo Art. · 2026-10-17](https://bsky.app/profile/yosuke5408.bsky.social/post/3my473jov432w): Tested the "R³" model for reconstructing 3D space and camera trajectories from video. Way faster than traditional SfM, and combining it with Depth Anything 3 works surprisingly well. Got it running in Colab and pushed the result into Blender.… https://yosuke4061.com/new\_toppage/articles/midori357/ - [Hacker News · doener · 2025-11-18](https://news.ycombinator.com/item?id=45965141): Depth Anything 3: Recovering the Visual Space from Any Views ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2025-11-20 - First published: 2025-11-13 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open depth-anything/depth-anything-3 on Hugging Face](https://huggingface.co/spaces/depth-anything/depth-anything-3) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c1e531c5-63e1-4315-a529-7fcae3cb7374) - [Its page on Mindket](https://mindket.com/p/c1e531c5-63e1-4315-a529-7fcae3cb7374)