AI GitHub
Nerfstudio for reconstructing scenes from images
Train and inspect neural scene representations from captured views.
Key Takeaways
- The project combines dataset processing, reconstruction methods and a viewer.
- Prepare a set of overlapping images and valid camera data or the inputs needed to estimate it.
- Capture an object from overlapping angles and hold out several views.
- Moving subjects and poor coverage can produce artifacts.
How it works
The project combines dataset processing, reconstruction methods and a viewer. Camera poses and input quality shape how well the scene can be rendered from new views.
What you need
Prepare a set of overlapping images and valid camera data or the inputs needed to estimate it. Save preprocessing outputs so scene training can be repeated.
What to try first
Capture an object from overlapping angles and hold out several views. Inspect the reconstruction and compare held-out renders with their source images.
Capture a simple scene with known camera locations. Check the estimated poses before tuning the representation, since reconstruction quality relies on those inputs.
Where to look for failures
Moving subjects and poor coverage can produce artifacts. Track camera calibration and data preprocessing before attributing an error to the model.
Related Franklin pages
Decide whether the goal is to generate an object or reconstruct a captured scene. Inspect the asset from new viewpoints and verify what your renderer can actually consume.
- Use differentiable Gaussian rasterization building blocks from Python. gsplat for Gaussian splatting research and rendering
- Generate a 3D representation from supported image or text conditioning workflows. TRELLIS for generating 3D assets
For video assembled from authored HTML and motion graphics, see Franklin’s HyperFrames profile. HyperFrames
Project documentation
Try it for your workflow
Explore the project’s documentation for setup and usage, or save this page to a collection for later.
Read the project documentation ↗