metadata
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 | Project Page | Code
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
@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