This repository contains pretrained checkpoints for SiMO (Single-Modal-Operable Multimodal Collaborative Perception), a novel framework for robust multimodal collaborative 3D object detection in autonomous driving.

📜 Paper

Title: Single-Modal-Operable Multimodal Collaborative Perception
Conference: ICLR 2026
OpenReview: Link
ArXiv：Link

🚀 Key Features

Single-Modal Operability: Maintains functional performance when one modality fails
LAMMA Fusion: Length-Adaptive Multi-Modal Fusion module
PAFR Training: Pretrain-Align-Fuse-Random Drop training strategy
Graceful Degradation: >80% AP@30 with camera-only operation

📦 Available Models

Model	Dataset	Architecture	Checkpoint
SiMO-PF	OPV2V-H	Pyramid Fusion + LAMMA	Download
SiMO-AttFuse	OPV2V-H	AttFusion + LAMMA	Download

📊 Performance

OPV2V-H (with Random Drop)

Modality	AP@30	AP@50	AP@70
LiDAR + Camera	98.30	97.94	94.64
LiDAR-only	97.32	97.07	94.06
Camera-only	80.81	69.63	44.82

💻 Usage

Installation

bash

git clone https://github.com/dempsey-wen/SiMO.git
cd SiMO
pip install -r requirements.txt

Download Checkpoint

bash

# Install huggingface-hub
pip install huggingface-hub

# Download specific checkpoint
python -c "from huggingface_hub import hf_hub_download; hf_hub_download(repo_id='DempseyWen/SiMO', filename='***.pth')"

📖 Full Documentation

For complete documentation, training scripts, and data preparation instructions, please visit our GitHub repository.

🏢 Acknowledgements

This work builds upon:

📄 Citation

If you find this work useful, please cite:

bibtex

@inproceedings{wen2026simo,
  title={Single-Modal-Operable Multimodal Collaborative Perception},
  author={Wen, Dempsey and Lu, Yifan and others},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2026}
}

📄 License

MIT License - see LICENSE for details.