COCO3D Dataset

The evaluation set of COCO3D and pseudo-labeled training set are available at Hugging Face.

3D BBox Human Refinement Interface

We release the source code for the refinement interface at https://github.com/UVA-Computer-Vision-Lab/3d_annotator.

Getting Started

📦 Installation Guide - Setup instructions and external dependencies

📖 COCO Pipeline Guide - Run the pipeline on COCO dataset

🔧 OVMono3D Fine-tuning - Code for fine-tuning OVMono3D on LabelAny3D pseudo annotations

Citing

If you find this work useful for your research, please kindly cite:

@inproceedings{yao2025labelany3d,
  title={LabelAny3D: Label Any Object 3D in the Wild},
  author={Jin Yao and Radowan Mahmud Redoy and Sebastian Elbaum and Matthew B. Dwyer and Zezhou Cheng},
  booktitle={Neural Information Processing Systems (NeurIPS)},
  year={2025}
}

@inproceedings{yao2025open,
  title={Open Vocabulary Monocular 3D Object Detection},
  author={Yao, Jin and Gu, Hao and Chen, Xuweiyi and Wang, Jiayun and Cheng, Zezhou},
  booktitle={Proceedings of the International Conference on 3D Vision (3DV)},
  year={2026}
}

Acknowledgements

This work builds on many open-source projects:

• Gen3DSR - 3D reconstruction framework
• TRELLIS - 3D asset generation
• MoGe - Monocular geometry estimation
• DepthPro - Metric depth estimation
• MASt3R - Dense matching
• InvSR - Image super-resolution
• COCONUT - COCO segmentation annotations
• OVMono3D - Open vocabulary monocular 3D detection

Licenses

This repository is licensed under Apache-2.0; see NOTICE for third-party attribution.

External models keep their own terms. Unrestricted:

Non-commercial, optional in our pipeline:

COCO images keep their original Flickr terms; COCO3D annotations are CC BY 4.0.