OpenLRM

Open-source implementation of Large Reconstruction Models for single-image 3D.

Open SourceSelf HostedOffline CapableGPU Required (8GB+ VRAM)
0.0 (0)

About

OpenLRM is an open source reimplementation of the Large Reconstruction Model, a feed-forward approach that turns a single image into a 3D representation in one pass rather than through slow per-shape optimization. Given one photo, it predicts a triplane NeRF and can export rendered videos and PLY mesh files. Released by 3DTopia, it comes in six pretrained variants across small, base, and large sizes, ranging from 12-layer models at 224px input up to 16-layer models with 1024 feature dimensions at 448px input, each trained on either Objaverse alone or Objaverse combined with MVImgNet, with Blender rendering scripts included for data preparation. The code is Apache 2.0 licensed, but the released weights are CC-BY-NC 4.0 and restricted to research use, so commercial products need their own training run. It serves researchers and graphics developers experimenting with fast single-image 3D reconstruction.

Should you use OpenLRM?

Pick it when

Pick OpenLRM when you are researching Large Reconstruction Models and want six pretrained sizes plus Blender data-prep scripts to compare or retrain, with Apache-2.0 code you can build on.

Look elsewhere when

Skip it for commercial output: the released weights are CC-BY-NC 4.0, so you would need to train your own. TripoSR follows the same LRM approach with MIT weights, and InstantMesh is Apache-2.0.

Alternatives to OpenLRM

  • TripoSR

    Same feed-forward LRM idea with MIT code and weights, so commercially usable out of the box, but fewer model sizes to study.

  • InstantMesh

    Adds a multi-view diffusion stage before LRM-style reconstruction, Apache-2.0 licensed, on similar 8 GB hardware.

  • Stable Fast 3D

    Builds on TripoSR with UV-unwrapped textures and material prediction for engine use, under a revenue-capped community license.

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Details

Price
Free
Platform
Local/Desktop
Difficulty
Intermediate (3/5)
License
Apache-2.0
Minimum VRAM
8 GB
Added
Apr 3, 2026

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