Towards Open-Vocabulary Multimodal 3D Object Detection with Attributes


Xinhao Xiang (University of California, Davis), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories (MERL)), Suhas Lohit (Mitsubishi Electric Research Laboratories (MERL)), Michael J. Jones (Mitsubishi Electric Research Laboratories (MERL)), Jiawei Zhang (University of California, Davis)
The 35th British Machine Vision Conference
PDFPosterVideo (Right click to download)Supplementary

Abstract

3D object detection plays a crucial role in autonomous systems, yet existing methods are limited by closed-set assumptions and struggle to recognize novel objects and their attributes in real-world scenarios. We propose OVODA, a novel framework enabling both open-vocabulary 3D object and attribute detection with no need to know the novel class anchor size. OVODA uses foundation models to bridge the semantic gap between 3D features and texts while jointly detecting attributes, e.g., spatial relationships, motion states, etc. To facilitate such research direction, we propose OVAD, a new dataset that supplements existing 3D object detection benchmarks with comprehensive attribute annotations. OVODA incorporates several key innovations, including foundation model feature concatenation, prompt tuning strategies, and specialized techniques for attribute detection such as perspective-specified prompts and horizontal flip augmentation. Our results on both the nuScenes and Argoverse 2 datasets show that under the condition of no given anchor sizes of novel classes, OVODA outperforms the state-of-the-art methods in open-vocabulary 3D object detection while successfully recognizing object attributes. Our OVAD dataset is released [here](https://doi.org/10.5281/zenodo.16904069).

Citation

@inproceedings{Xiang_2025_BMVC,
author    = {Xinhao Xiang and Kuan-Chuan Peng and Suhas Lohit and Michael J. Jones and Jiawei Zhang},
title     = {Towards Open-Vocabulary Multimodal 3D Object Detection with Attributes},
booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
publisher = {BMVA},
year      = {2025},
url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_649/paper.pdf}
}


Copyright © 2025 The British Machine Vision Association and Society for Pattern Recognition
The British Machine Vision Conference is organised by The British Machine Vision Association and Society for Pattern Recognition. The Association is a Company limited by guarantee, No.2543446, and a non-profit-making body, registered in England and Wales as Charity No.1002307 (Registered Office: Dept. of Computer Science, Durham University, South Road, Durham, DH1 3LE, UK).

Imprint | Data Protection