Bavantha11 / m2h-mx

M2H-MX Multi-Task Weights

This repository hosts model-only weights for M2H-MX: Multi-Task Semantic and Geometric Perception for Real-Time Monocular 3D Scene Graph Construction .

M2H-MX is a multi-task dense visual perception model , not a semantic-segmentation-only model. Given a monocular RGB image, the network can predict:

• metric depth or disparity, depending on the dataset configuration;
• semantic segmentation logits;
• surface normals;
• edge maps.

Depth and semantics are the primary deployment outputs used by Mono-Hydra++ or a compatible mapping backend for metric-semantic mapping and downstream 3D scene graph construction. Surface normals and edges are auxiliary training heads used to improve geometric and semantic consistency. The network improves the dense evidence used by the mapping backend; it does not directly predict the 3D scene graph.

Code and instructions: https://github.com/BavanthaU/m2h_mx

Artifacts

These are model-only state dictionaries. They do not include optimizer, scheduler, gradient scaler, or EMA state.

Download

From the code repository:

python3 scripts/download_weights.py --repo-id Bavantha11/m2h-mx --verify

Citation

@misc{udugama2026m2hmxmultitaskdensevisual,
  title={M2H-MX: Multi-Task Semantic and Geometric Perception for Real-Time Monocular 3D Scene Graph Construction},
  author={U. V. B. L. Udugama and George Vosselman and Francesco Nex},
  year={2026},
  eprint={2603.29236},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2603.29236},
}

@misc{udugama2026monohydrarealtimemonocularscene,
  title={Mono-Hydra++: Real-Time Monocular Scene Graph Construction with Multi-Task Learning for 3D Indoor Mapping},
  author={U. V. B. L. Udugama and George Vosselman and Francesco Nex},
  year={2026},
  eprint={2605.17661},
  archivePrefix={arXiv},
  primaryClass={cs.RO},
  url={https://arxiv.org/abs/2605.17661},
}