sam-3d-objects README

The project's README from github.com/facebookresearch/sam-3d-objects. As one markdown file: README.md.

SAM 3D

SAM 3D Objects is one part of SAM 3D, a pair of models for object and human mesh reconstruction. If you’re looking for SAM 3D Body, click here.

SAM 3D Objects

SAM 3D Team, Xingyu Chen*, Fu-Jen Chu*, Pierre Gleize*, Kevin J Liang*, Alexander Sax*, Hao Tang*, Weiyao Wang*, Michelle Guo, Thibaut Hardin, Xiang Li⚬, Aohan Lin, Jia-Wei Liu, Ziqi Ma⚬, Anushka Sagar, Bowen Song⚬, Xiaodong Wang, Jianing Yang⚬, Bowen Zhang⚬, Piotr Dollár†, Georgia Gkioxari†, Matt Feiszli†§, Jitendra Malik†§

Meta Superintelligence Labs

*Core contributor (Alphabetical, Equal Contribution), ⚬Intern, †Project leads, §Equal Contribution

[Paper] [Code] [Website] [Demo] [Blog] [BibTeX] [Roboflow]

SAM 3D Objects is a foundation model that reconstructs full 3D shape geometry, texture, and layout from a single image, excelling in real-world scenarios with occlusion and clutter by using progressive training and a data engine with human feedback. It outperforms prior 3D generation models in human preference tests on real-world objects and scenes. We released code, weights, online demo, and a new challenging benchmark.

Latest updates

Installation

Follow the setup steps before running the following.

Single or Multi-Object 3D Generation

SAM 3D Objects can convert masked objects in an image, into 3D models with pose, shape, texture, and layout. SAM 3D is designed to be robust in challenging natural images, handling small objects and occlusions, unusual poses, and difficult situations encountered in uncurated natural scenes like this kidsroom:

For a quick start, run python demo.py or use the the following lines of code:

import sys

# import inference code
sys.path.append("notebook")
from inference import Inference, load_image, load_single_mask

# load model
tag = "hf"
config_path = f"checkpoints/{tag}/pipeline.yaml"
inference = Inference(config_path, compile=False)

# load image and mask
image = load_image("notebook/images/shutterstock_stylish_kidsroom_1640806567/image.png")
mask = load_single_mask("notebook/images/shutterstock_stylish_kidsroom_1640806567", index=14)

# run model
output = inference(image, mask, seed=42)

# export gaussian splat
output["gs"].save_ply(f"splat.ply")

For more details and multi-object reconstruction, please take a look at out two jupyter notebooks:

SAM 3D Body

SAM 3D Body (3DB) is a robust promptable foundation model for single-image 3D human mesh recovery (HMR).

As a way to combine the strengths of both SAM 3D Objects and SAM 3D Body, we provide an example notebook that demonstrates how to combine the results of both models such that they are aligned in the same frame of reference. Check it out here.

License

The SAM 3D Objects model checkpoints and code are licensed under SAM License.

Contributing

See contributing and the code of conduct.

Contributors

The SAM 3D Objects project was made possible with the help of many contributors.

Robbie Adkins, Paris Baptiste, Karen Bergan, Kai Brown, Michelle Chan, Ida Cheng, Khadijat Durojaiye, Patrick Edwards, Daniella Factor, Facundo Figueroa, Rene de la Fuente, Eva Galper, Cem Gokmen, Alex He, Enmanuel Hernandez, Dex Honsa, Leonna Jones, Arpit Kalla, Kris Kitani, Helen Klein, Kei Koyama, Robert Kuo, Vivian Lee, Alex Lende, Jonny Li, Kehan Lyu, Faye Ma, Mallika Malhotra, Sasha Mitts, William Ngan, George Orlin, Peter Park, Don Pinkus, Roman Radle, Nikhila Ravi, Azita Shokrpour, Jasmine Shone, Zayida Suber, Phillip Thomas, Tatum Turner, Joseph Walker, Meng Wang, Claudette Ward, Andrew Westbury, Lea Wilken, Nan Yang, Yael Yungster

Citing SAM 3D Objects

If you use SAM 3D Objects in your research, please use the following BibTeX entry.

@article{sam3dteam2025sam3d3dfyimages,
      title={SAM 3D: 3Dfy Anything in Images}, 
      author={SAM 3D Team and Xingyu Chen and Fu-Jen Chu and Pierre Gleize and Kevin J Liang and Alexander Sax and Hao Tang and Weiyao Wang and Michelle Guo and Thibaut Hardin and Xiang Li and Aohan Lin and Jiawei Liu and Ziqi Ma and Anushka Sagar and Bowen Song and Xiaodong Wang and Jianing Yang and Bowen Zhang and Piotr Dollár and Georgia Gkioxari and Matt Feiszli and Jitendra Malik},
      year={2025},
      eprint={2511.16624},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2511.16624}, 
}

sam-3d-objects (facebookresearch/sam-3d-objects)

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7.5K ·GitHub
Sam-3d-objects
What is this?SAM 3D Objects is a foundation model from Meta that reconstructs full 3D shape geometry, texture, and object layout from a single 2D image, even in cluttered real-world scenes with occlusion.
Is this a model I run myself, or just a hosted demo?It is open research code and model checkpoints you run yourself (via a script or notebooks), plus an online demo and benchmark released alongside it. It is one half of the SAM 3D pair, the other being SAM 3D Body for human mesh reconstruction.
How do I try it on my own image?After following the setup steps, run `python demo.py`, or use the single-object and multi-object Jupyter notebooks included in the repo for more detailed reconstruction workflows.
What do I need installed before running it?The README points to separate setup steps that must be completed before running the demo script or notebooks, but the exact dependency list is not detailed in this excerpt.
Is the code free to use commercially?The model checkpoints and code are released under a custom 'SAM License' rather than a standard open-source license like MIT or Apache, so usage terms differ from typical permissive licenses.
Who is this built for?It targets computer vision and robotics researchers/engineers who need single-image 3D object reconstruction, as evidenced by the benchmark, leaderboard, and dense academic citation list.
How does SAM 3D Objects relate to SAM 3D Body?They are a paired pair of models from the same project: SAM 3D Objects reconstructs objects while SAM 3D Body handles human mesh recovery, and the repo includes a notebook showing how to align both outputs in the same frame of reference.
What makes SAM 3D Objects better than prior 3D generation models?It uses progressive training and a data engine with human feedback, and it outperforms prior 3D generation models in human preference tests specifically on real-world objects and scenes rather than clean studio images.
Can it handle more than one object in an image?Yes, there is a dedicated multi-object Jupyter notebook for reconstructing several masked objects from a single image in addition to the single-object workflow.
Is there a way to benchmark or compare results?The project ships a challenging benchmark and, as of the latest update, a 3D Artist Object Set with a Hugging Face Leaderboard for comparing model results.
What are similar projects?ml-sharp, WorldSculpt, vggt, Mira-Scene, pointdit, wuji-ego-mint, InsertAny3D, shap-e
Which topics is it in?meta, image-to-3d, demo, 3d-reconstruction, foundation-models, jupyter-notebook, paper, robotics, 3d, academic-research, generative-ai, computer-vision
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facebookresearch
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Updated
CommitJun 02
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Commits · 90 Days