---
tags:
  - 3d-object-detection
  - open-vocabulary
  - point-cloud
datasets:
  - lvis
  - sunrgbd
  - scannet
pipeline_tag: object-detection
---

# ImOV3D: Learning Open-Vocabulary Point Clouds 3D Object Detection from Only 2D Images

**NeurIPS 2024** | [Paper](https://arxiv.org/abs/2410.24001) | [Project Page](https://yangtiming.github.io/ImOV3D_Page/) | [Code](https://github.com/yangtiming/ImOV3D)

> Timing Yang\*, Yuanliang Ju\*, Li Yi  
> Shanghai Qi Zhi Institute, IIIS Tsinghua University, Shanghai AI Lab

## Overview

ImOV3D is the **first open-vocabulary 3D object detector trained entirely from 2D images** — no 3D ground truth required. It bridges the 2D-3D modality gap via flexible modality conversion: lifting 2D images to pseudo point clouds (monocular depth estimation) and rendering point clouds back to pseudo images (ControlNet). This creates a unified image-PC representation for training a multimodal 3D detector.

## Citation

```bibtex
@article{yang2024imov3d,
  title={ImOV3D: Learning Open Vocabulary Point Clouds 3D Object Detection from Only 2D Images},
  author={Yang, Timing and Ju, Yuanliang and Yi, Li},
  journal={Advances in Neural Information Processing Systems},
  volume={37},
  pages={141261--141291},
  year={2024}
}
```

## Contact

Timing Yang: timingya@usc.edu · Yuanliang Ju: yuanliang.ju@mail.utoronto.ca