🚀 Fast-SAM3D: 3Dfy Anything in Images but Faster

Weilun Feng ^* ,Mingqiang Wu^*, Zhiliang Chen, Chuanguang Yang^✉, Haotong Qin, Yuqi Li, Xiaokun Liu, Guoxin Fan, Zhulin An^✉, Libo Huang, Yulun Zhang, Michele Magno, Yongjun Xu

^*Equal Contribution ^✉Corresponding Author

Institute of Computing Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, China University of Mining and Technology, ETH Zürich, Shanghai Jiao Tong University

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Fast-SAM3D accelerates SAM3D by up to 2.67× while maintaining geometric fidelity and semantic consistency.

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💡 TL;DR

Fast-SAM3D is a training-free acceleration framework for single-view 3D reconstruction that delivers up to 2.67× speedup with negligible quality loss. Our approach dynamically aligns computation with instantaneous generation complexity through three heterogeneity-aware mechanisms.

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📋 Table of Contents

1. News
2. Highlights
3. Method Overview
4. Installation
5. Usage
6. Results
7. Citation
8. Acknowledgements

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📰 News

• [2026.02.05] 🎉 Paper and code released! Check out our paper.

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🌟 Highlights

1. 🚀 Training-Free Acceleration: Achieves 2.67× speedup for single-object generation and 2.01× for scene generation without any model retraining.

2. 🎯 Heterogeneity-Aware Design: Addresses multi-level heterogeneity in 3D generation pipelines: kinematic distinctiveness, intrinsic sparsity, and spectral variance.

3. 🔧 Plug-and-Play Modules: Three seamless integration modules:

   • Modality-Aware Step Caching: Decouples shape evolution from sensitive layout updates
   • Joint Spatiotemporal Token Carving: Concentrates refinement on high-entropy regions
   • Spectral-Aware Token Aggregation: Adapts decoding resolution to geometric complexity

4. ✨ Quality Preservation: Maintains or even exceeds original model's geometric fidelity (F-Score: 92.59 vs. 92.34).

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🔍 Method Overview

Overview of Fast-SAM3D. Our approach integrates three heterogeneity-aware modules: (1) Modality-Aware Step Caching for decoupling structural evolution from layout updates; (2) Joint Spatiotemporal Token Carving for eliminating redundancy; (3) Spectral-Aware Token Aggregation for adaptive decoding resolution.

Stage 1: Modality-Aware Step Caching

The Sparse Structure Generator exhibits modality heterogeneity: shape tokens evolve smoothly while layout tokens are volatile. We propose:

• Linear Extrapolation for shape tokens using finite-difference prediction
• Momentum-Anchored Smoothing for layout tokens to suppress high-frequency jitter

Stage 2: Joint Spatiotemporal Token Carving

The SLaT Generator shows intrinsic refinement sparsity: updates concentrate on high-entropy regions. We design:

• Unified Saliency Potential combining temporal dynamics (magnitude & abruptness) and spatial frequency
• Dynamic Adaptive Step Caching with curvature-aware trajectory approximation

Stage 3: Spectral-Aware Token Aggregation

The Mesh Decoder processes dense token sequences. We introduce:

• Spectral Complexity Analysis using High-Frequency Energy Ratio (HFER)
• Instance-Adaptive Aggregation with aggressive compression for simple shapes and detail preservation for complex geometries

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🛠️ Installation

Requirements

• Python >= 3.9
• PyTorch >= 2.0
• CUDA >= 11.8
• SAM3D dependencies

Setup FastSAM3D Environment

# create fastsam3d environment
mamba env create -f environments/default.yml
mamba activate fastsam3d

# for pytorch/cuda dependencies
export PIP_EXTRA_INDEX_URL="https://pypi.ngc.nvidia.com https://download.pytorch.org/whl/cu121"

# install fastsam3d and core dependencies
pip install -e '.[dev]'
pip install -e '.[p3d]' # pytorch3d dependency on pytorch is broken, this 2-step approach solves it

# for inference
export PIP_FIND_LINKS="https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-2.5.1_cu121.html"
pip install -e '.[inference]'

# patch things that aren't yet in official pip packages
./patching/hydra # https://github.com/facebookresearch/hydra/pull/2863

If you encounter difficulties during installation, please refer to the more detailed /doc/Setup.md documentation.

Getting Checkpoints

From HuggingFace

⚠️ Before using FastSAM 3D , please request access to the checkpoints on the SAM 3D Objects Hugging Face repo. Once accepted, you need to be authenticated to download the checkpoints. You can do this by running the following steps (e.g. hf auth login after generating an access token).

⚠️ SAM 3D Objects is available via HuggingFace globally, except in comprehensively sanctioned jurisdictions. Sanctioned jurisdiction will result in requests being rejected.

pip install 'huggingface-hub[cli]<1.0'

TAG=hf
hf download \
  --repo-type model \
  --local-dir checkpoints/${TAG}-download \
  --max-workers 1 \
  facebook/sam-3d-objects
mv checkpoints/${TAG}-download/checkpoints checkpoints/${TAG}
rm -rf checkpoints/${TAG}-download

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🚀 Usage

Quick Start/Object Generation

# Generate 3D from single image + mask
cd notebook
python infer.py \
    --image_path examples/input.jpg \
    --mask_index 1 \
    --output_dir outputs/ \
    --enable_acceleration

Acceleration Options

# Full Fast-SAM3D acceleration (default)
cd notebook
python infer.py \
    --image_path examples/image.png \
    --mask_index 1\
    --enable_ss_cache \
    --enable_slat_carving \
    --enable_mesh_aggregation

# Customize acceleration strength
cd notebook
python infer.py \
    --image_path examples/image.png \
    --mask_index 1 \
    --output_dir /data3/wmq/Fast-sam3d-objects/Look \
    --ss_cache_stride 3 \
    --ss_warmup 2 \
    --ss_order 1 \
    --ss_momentum_beta 0.5 \
    --slat_thresh 1.5 \
    --slat_warmup 3 \
    --slat_carving_ratio 0.1 \
    --mesh_spectral_threshold_low 0.5 \
    --mesh_spectral_threshold_high 0.7 \
    --enable_acceleration

Scene Generation

cd notebook
python infer_scene.py \
    --image_dir examples \
    --output_dir outputs/ \
    --enable_acceleration

Image Directory

├── example/
│   ├── image.png
│   ├── 0.png
│   └── 1.png

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📊 Results

Quantitative Comparison

Method	Visual ↑	CD ↓	F1@0.05 ↑	vIoU ↑	3D-IoU ↑	Scene Time ↓	Speed ↑
SAM3D	0.369	0.022	92.34	0.543	0.403	462.3s	1.00×
Random Drop	0.264	0.030	83.52	0.327	0.094	402.2s	1.15×
Uniform Merge	0.329	0.023	91.48	0.540	0.367	366.8s	1.26×
Fast3Dcache	0.348	0.022	91.31	0.505	0.051	443.3s	1.04×
TaylorSeer	0.344	0.028	90.95	0.504	0.374	265.6s	1.74×
EasyCache	0.342	0.028	87.06	0.432	0.186	244.9s	1.89×
Fast-SAM3D	0.350	0.022	92.59	0.552	0.375	229.7s	2.01×

Speedup Analysis

Qualitative Comparison

Fast-SAM3D produces results perceptually indistinguishable from SAM3D while generic strategies suffer from structural collapse (Random Drop) or semantic drift (TaylorSeer).

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📄 Citation

If you find this work helpful, please consider citing:

@misc{feng2026fastsam3d3dfyimagesfaster,
      title={Fast-SAM3D: 3Dfy Anything in Images but Faster}, 
      author={Weilun Feng and Mingqiang Wu and Zhiliang Chen and Chuanguang Yang and Haotong Qin and Yuqi Li and Xiaokun Liu and Guoxin Fan and Zhulin An and Libo Huang and Yulun Zhang and Michele Magno and Yongjun Xu},
      year={2026},
      eprint={2602.05293},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2602.05293}, 
}

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🙏 Acknowledgements

This project is built upon the excellent SAM3D framework. We thank the authors for their outstanding work in open-world 3D reconstruction.

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📜 License

This project is released under the MIT License.

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📧 Contact

For questions or suggestions, please open an issue or contact:

• Weilun Feng: fengweilun24s@ict.ac.cn

• Mingqiang Wu wumingqiang25e@ict.ac.cn

• Chuanguang Yang: yangchuanguang@ict.ac.cn

• Zhulin An: anzhulin@ict.ac.cn

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⭐ Star us on GitHub if you find this project helpful!