Appofficial publisher

TripoSR

App : A product you use directly

Image to 3D in under one second, open source, runs locally.

The fastest and lightest of the local 3D models.

Official site GitHub · 7,213 ★

Overall score

7.7 /10

Security: nothing to flag

Utility

8.0

Reliability

9.0

Security

No caveat on record

Ease

Technical · Technical

Security reflects where data goes, requested permissions and incidents on record at the verification date. It is not a guarantee of zero risk: check the deployment mode and the connections you enable.

What it does, concretely

Creating a 3D model from a single photo is usually a lengthy and technical task, reserved for specialized software or paid online services. TripoSR addresses this by generating a complete 3D object from a single image in under half a second on a modern GPU.

In practice, you provide an input image and the model produces a 3D file with vertex colors or an exportable texture. Processing happens entirely on your local machine, via a command-line interface or a Gradio graphical interface accessible in your browser. The model requires approximately 6 GB of VRAM for a single image.

TripoSR is aimed at developers, researchers, and creators who want to integrate 3D reconstruction into their projects without relying on a cloud service. It is particularly useful for rapidly prototyping 3D assets from photos of real objects.

However, installation requires Python, PyTorch, and a precise CUDA configuration, making it less accessible to users without technical experience. Those looking for a turnkey solution without a command line will find it difficult to get started.

What it offers

·3D reconstruction from a single image in under 0.5 seconds on an NVIDIA A100 GPU.
·Export of the 3D model with vertex colors or baked texture depending on the chosen option.
·Configurable texture resolution via the --texture-resolution option.
·Processing of multiple images in a single command by separating paths with spaces.
·Local graphical interface via Gradio, accessible from a browser without additional configuration.
·Online demo available on Hugging Face Spaces for testing without installation.
·Source code, pre-trained models, and demo released under the MIT license.

Our tips

Use the --bake-texture option to get a UV texture rather than vertex colors, which gives a more usable result in most 3D software.
Adjust --texture-resolution to control the quality of the exported texture according to your file size and detail requirements.
Make sure the major CUDA version installed on your machine exactly matches the one used by your PyTorch build, to avoid errors during mesh computation.
Run python gradio_app.py to access a visual interface in your browser if you prefer to avoid the command line.
Check python run.py --help to discover all available options before running your first reconstructions.

Watch out

Installation requires an exact match between the local CUDA major version and the PyTorch version, a common error that blocks GPU computation.
Approximately 6 GB of VRAM are required for a single image, which excludes machines without a dedicated GPU or with an entry-level graphics card.

In practice

Price: Free
Commercial use: Commercial use: yes
Available on: linux, windows
Licence: MIT
Runs locally: Yes, 6 GB VRAM recommended
FR interface: No

Security and data

Rating: Security: nothing to flag
Your data: Data stays local
Maintenance: Active

Links and repository checked on 25 October 2026 · added on 1 March 2024

How to install TripoSR

1. Install Python 3.8 or higher on your machine. 2. Install CUDA if your GPU supports it, noting the major version used. 3. Install PyTorch making sure to choose the version compiled with the same CUDA major version (see pytorch.org/get-started/locally). 4. Update setuptools with pip install --upgrade setuptools. 5. Clone the TripoSR GitHub repository and install dependencies with pip install -r requirements.txt. 6. Run a reconstruction with python run.py your_image.png --output-dir output/ and verify that the generated files appear in the output folder.

pip install -r requirements.txt

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