apchenstu/TensoRF

Health v2 · maintenance only

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Adoption not part of the score

1239 stars · 156 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

TensoRF is a PyTorch implementation of the ECCV 2022 paper 'TensoRF: Tensorial Radiance Fields', which models and reconstructs radiance fields using tensor decomposition (VM and CP). It offers fast training, compact memory footprint, and state-of-the-art novel view synthesis quality, with mesh export support.

Use cases

• reconstruct 3d scenes from images
• novel view synthesis from photos
• train a radiance field on NeRF datasets
• render images from a pretrained checkpoint
• export a mesh from a trained radiance field
• compare VM and CP tensor decomposition for radiance fields

When to choose

• you need fast NeRF-style training with low memory usage
• you want high-quality novel view synthesis on standard datasets like Synthetic-NeRF, NSVF, Tanks&Temples, or LLFF
• you need to extract meshes from radiance fields
• you want a research baseline for tensor-based radiance field methods

When to avoid

• you need real-time rendering on consumer hardware without a GPU
• you want a production-ready 3D reconstruction pipeline with a GUI
• you need dynamic or time-varying scenes (TensoRF targets static scenes)
• you prefer Gaussian splatting or mesh-based reconstruction workflows

Facets

library · maturity stable

machine-learning deep-learning graphics simulation computer-vision graphics machine-learning deep-learning python radiance-fields neural-rendering 3d-reconstruction novel-view-synthesis tensor-decomposition pytorch eccv-2022 linux gpu

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