Argus is a feed-forward network for metric panoramic 3D reconstruction of indoor scenes. Given sparse, unordered panoramic captures, it jointly predicts camera pose, metric depth, and point cloud reconstruction in a shared metric world frame.
This repository hosts the pretrained Argus weights (argus_realsee3d.pt).
- Paper: Argus: Metric Panoramic 3D Reconstruction for Indoor Scenes (arXiv:2606.30047)
- Project page: https://argus-paper.realsee.ai/
- Code & usage: https://github.com/realsee-developer/Argus
- Realsee3D dataset: https://dataset.realsee.ai/
- Authors: Xi Li, Linyuan Li, Yan Wu, Tong Rao, Kai Zhang, Xinchen Hui, Cihui Pan
- License: CC BY-NC 4.0
Overview
Metric feed-forward 3D reconstruction for panoramic data has remained under-explored due to the lack of large-scale panoramic RGB-D training data. This work introduces:
- Realsee3D — a hybrid dataset of 10K indoor scenes (1K real, 9K synthetic) with 299K panoramic viewpoints and precise metric annotations.
- Argus — a feed-forward network trained on Realsee3D for metric panoramic 3D reconstruction.
In the sparse unordered capture setting of Realsee3D, a poorly chosen coordinate anchor can cause global pose drift. Argus addresses this with a learned covisibility module that selects the geometrically optimal reference view to anchor the metric world frame. To improve multi-task learning, the bidirectional pixel-to-world mapping is decomposed into interpretable sub-steps with per-step supervision and cross-coordinate joint constraints, reinforcing geometric consistency across prediction branches.
On the Realsee3D benchmark, Argus achieves state-of-the-art metric performance in camera pose estimation, depth estimation, and point cloud reconstruction.
Files
| File | Description |
|---|---|
argus_realsee3d.pt | Pretrained Argus model weights (PyTorch checkpoint) |
Usage
For the model architecture, inference pipeline, and example code, see the official repository: https://github.com/realsee-developer/Argus
Citation
If you use Argus or the Realsee3D dataset, please cite:
@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},
}