---
license: cc-by-nc-4.0
library_name: pytorch
pipeline_tag: image-to-3d
tags:
  - 3d-reconstruction
  - panoramic
  - depth-estimation
  - camera-pose-estimation
  - point-cloud
  - indoor-scenes
  - feed-forward
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---

<div align="center">

<img src="https://raw.githubusercontent.com/realsee-developer/Argus/main/assets/argus_logo.png" width="200">

<h3>Argus: Metric Panoramic 3D Reconstruction for Indoor Scenes</h3>

<h4>ECCV 2026</h4>

<a href="https://argus-paper.realsee.ai" target="_blank"><img src="https://img.shields.io/badge/Project_Page-green" alt="Project Page"></a>
<a href="https://arxiv.org/abs/2606.30047" target="_blank"><img src="https://img.shields.io/badge/arXiv-2606.30047-b31b1b" alt="arXiv"></a>
<a href="https://huggingface.co/RealseeTechnology/argus-realsee3d" target="_blank"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-yellow" alt="HuggingFace Model"></a>
<a href="https://huggingface.co/spaces/RealseeDeveloper/Argus" target="_blank"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo-blue" alt="HuggingFace Demo"></a>
<a href="https://github.com/realsee-developer/RealSee3D" target="_blank"><img src="https://img.shields.io/badge/RealSee3D-Dataset-orange" alt="RealSee3D Dataset"></a>


<p><b><a href="https://www.realsee.ai">Realsee</a></b></p>
</div>

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](https://arxiv.org/abs/2606.30047) (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:

```bibtex
@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}, 
}
```