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RealseeTechnology
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argus-realsee3d

Image-to-3D
PyTorch
3d-reconstruction
panoramic
depth-estimation
camera-pose-estimation
point-cloud
indoor-scenes
feed-forward
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Argus: Metric Panoramic 3D Reconstruction for Indoor Scenes

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).

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}, 
}
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Paper for RealseeTechnology/argus-realsee3d