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Gamba

Gamba is a Python and PyTorch implementation of a model that combines Gaussian splatting and Mamba for single-view 3D reconstruction.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Gamba — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Gamba when you want to inspect or experiment with a PyTorch implementation for single-view 3D reconstruction, rather than access a hosted reconstruction tool.

Who it is for

  • Researchers exploring single-view 3D reconstruction.
  • Developers experimenting with the Gamba model in PyTorch.

Documented uses

  • Install the gamba-torch package.
  • Instantiate the Gamba model with the documented parameters.
  • Pass a tensor through the model and inspect the forward-pass output.
Before you choose

Requirements & limits

  • The README provides a package installation command and a concrete forward-pass example.
  • The example uses a synthetic tensor and does not document image-to-3D reconstruction steps.
  • The repository states that the code is licensed under MIT.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install Gamba and run the documented forward-pass example

Set up the package and pass the README's example tensor through a Gamba model.

Before you start

  • Python environment with PyTorch available; the example imports torch.
  • Install the gamba-torch package.

Steps

  1. Install the package with the command below.

    Documented command
    pip install gamba-torch

  2. Import torch and Gamba from gamba.main.

  3. Create a random tensor with torch.randn(1, 1000, 512).

  4. Construct Gamba with dim=512, d_state=512, d_conv=512, n=16384 and depth=3, then call the model with the tensor and print the output.

Expected outcome

The example invokes Gamba's forward pass on a tensor and prints its output.

Guide limitations

  • The example does not demonstrate loading an image or producing a reconstructed 3D asset.
  • The README does not specify further environment or dependency requirements.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
17 October 2026
Original website title
kyegomez/Gamba · Official homepage title · retained in its source language
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