GeoSAM2

Unleashing the Power of SAM2 for 3D Part Segmentation  ·  CVPR 2026

GeoSAM2 lifts SAM2 from images to 3D meshes. Given a multi-view rendering of a mesh and an interactive prompt (a single 2D click or a 2D mask) on one view, it propagates a consistent segmentation across all views and back-projects the result to per-face 3D part labels.

This repository hosts the pretrained inference checkpoint (geosam2.pt). Code, configs, and a small multi-view demo dataset live in the companion GitHub repo: https://github.com/VAST-AI-Research/GeoSAM2.

Model summary

Task	Interactive 3D part segmentation on meshes via multi-view 2D propagation
Base model	facebook/sam2.1-hiera-base-plus
Architecture	SAM2 (Hiera-B+ image encoder + memory attention + mask decoder), plus a dedicated position-map encoder for 3D geometry, feature fusion, and LoRA adapters on the image and position-map encoders
Parameters	~154 M (fp32: ~588 MB · bf16: ~294 MB)
Input modalities	12 rendered views per mesh: color (.webp), depth (.exr), normal (.webp), camera metadata (meta.json)
Prompts	2D point clicks or a 2D mask on any view
Output	Per-view 2D label maps and per-face 3D labels for the input mesh
Render config	12 azimuthally-spaced views at 1024×1024 from a fixed elevation

Quickstart

# 1. Clone the code
git clone https://github.com/VAST-AI-Research/GeoSAM2.git
cd GeoSAM2
pip install -r requirements.txt
pip install -e .   # builds the optional CUDA op; set GEOSAM2_BUILD_CUDA=0 to skip

# 2. Download the checkpoint into ./ckpt
hf download VAST-AI/GeoSAM2 geosam2.pt --local-dir ckpt

# 3. Run the bundled demo (single-view point prompt -> 3D segmentation)
bash scripts/run_example.sh

Direct inference from a 2D mask:

python inference.py \
  --data-root example/sample_00 \
  --mask-path outputs/sample_00/2d_seg/mask_view0000.npy \
  --mask-view 0 \
  --postprocess-pa 0.02 \
  --output-dir outputs/sample_00/3d_seg

See the README for the full usage guide, including rendering your own meshes with Blender.

Files

File	Size	Description
geosam2.pt	~588 MB	Pretrained weights in float32 ({"model": state_dict}). Default choice.
geosam2-bf16.pt	~294 MB	Same weights cast to bfloat16 for faster downloads / lower memory. Loaded by the standard code path — load_state_dict upcasts to the model dtype, so no extra steps are required. Expect a small reconstruction error of ≤ 0.015 per weight versus the fp32 file.

Both checkpoints are loaded by sam2.build_sam.build_sam2_video_predictor_geosam2 with the Hydra config sam2/configs/geosam2.yaml. Pass the chosen file via --sam2-checkpoint (or use the default ckpt/geosam2.pt path expected by the scripts).

Intended use

• Intended: interactive 3D part segmentation of single-object meshes for research and content-creation tooling.
• Out of scope: scene-level segmentation, dynamic scenes, semantic category prediction (the model produces instance-level part masks, not semantic class labels), and safety-critical applications.

Limitations

• Expects the 12-view rendering convention produced by geosam2_render.py; arbitrary view counts or camera trajectories may degrade quality.
• The mesh must fit within the normalised cube used at render time (geosam2_render.py handles this for the bundled samples).
• Performance on thin/wire-like geometry and on highly transparent surfaces is still an open problem.
• The post-processing --postprocess-pa value sometimes needs hand-tuning per mesh (0.01, 0.02, 0.035 are useful starting points).

License

Released under the Apache License 2.0. The checkpoint is a derivative of Meta's SAM2 (Apache 2.0); see the NOTICE file in the code repo for attribution.

Citation

@article{deng2025geosam2,
  title   = {GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation},
  author  = {Deng, Ken and Yang, Yunhan and Sun, Jingxiang and
             Liu, Xihui and Liu, Yebin and Liang, Ding and Cao, Yan-Pei},
  journal = {arXiv preprint arXiv:2508.14036},
  year    = {2025}
}