Argus: Metric Panoramic 3D Reconstruction for Indoor Scenes

ECCV 2026

Realsee

Quick Start

Clone the repository and install dependencies:

git clone https://github.com/realsee-developer/Argus.git
cd Argus
pip install -r requirements.txt
pip install -e .

Download the pretrained weights:

# Authenticate with HuggingFace (required for gated model)
hf auth login
# Option 1: Auto-download via Python (cached by huggingface_hub)
python -c "from huggingface_hub import hf_hub_download; hf_hub_download(repo_id='RealseeTechnology/argus-realsee3d', filename='argus_realsee3d.pt')"# Option 2: Manual download
mkdir -p models
hf download RealseeTechnology/argus-realsee3d argus_realsee3d.pt --local-dir models

Run inference with a few lines of code:

importtorchfromhuggingface_hubimporthf_hub_downloadfromargus.models.argusimportArgusfromargus.utils.pose_encimportpose_encoding_to_extri360# Download model weights (requires: hf auth login)model_path=hf_hub_download(
repo_id="RealseeTechnology/argus-realsee3d",
filename="argus_realsee3d.pt",
)
# Load modelmodel=Argus(reorder_by_learning_ref=True, restore_metric_scale=True)
model.load_state_dict(torch.load(model_path)["model"], strict=False)
model.eval().cuda()
# Prepare input: panoramic images as tensor [S, 3, H, W], values in [0, 1]images= ... # your preprocessed ERP imageswithtorch.no_grad(), torch.amp.autocast("cuda", dtype=torch.bfloat16):
predictions=model(images.cuda())
# Extract camera extrinsicsextrinsic, conf=pose_encoding_to_extri360(pose_encoding=predictions["pose_enc"])
# Access depth and 3D pointsdepth=predictions["depth"] # [B, S, H, W, 1]depth_conf=predictions["depth_conf"] # [B, S, H, W]

Interactive Demo

Launch the Gradio demo for interactive 3D reconstruction and metric measurement:

# Model will be auto-downloaded from HuggingFace if not found locally# (requires: hf auth login)
python demo_gradio.py
# Or specify a local model path
python demo_gradio.py --model_path models/argus_realsee3d.pt

The demo supports:

• Uploading multiple panoramic images
• Real-time 3D reconstruction with GLB export
• Interactive metric distance measurement between points
• Adjustable confidence thresholds and visualization options

Evaluation

Evaluate on the Realsee3D benchmark:

cd evaluation
python eval.py \
--model_path ../models/argus_realsee3d.pt \
--dataset_path /path/to/Realsee3D \
--split both \
--ref

Metrics include camera pose accuracy, depth error, point map quality, and covisibility estimation.

Training

Training supports multi-GPU distributed training:

cd training
torchrun --nproc_per_node=8 launch.py --config full

See training/config/full.yaml for the full training configuration.

License

This project is licensed under the Apache License 2.0. The pretrained model weights trained on RealSee3D are released under a non-commercial license, consistent with the RealSee3D dataset license.

Citation

@misc{li2026argusmetricpanoramic3d,
title={Argus: Metric Panoramic 3D Reconstruction for Indoor Scenes}, author={Xi Li and Linyuan Li and Yan Wu and Tong Rao and Kai Zhang and Xinchen Hui and Cihui Pan},
year={2026},
eprint={2606.30047},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2606.30047}, }

Acknowledgements

Argus builds upon VGGT.