Create README.md

README.md

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+---
+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