2023-12-02-lu23a.md

title	section	openreview	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
OVIR-3D: Open-Vocabulary 3D Instance Retrieval Without Training on 3D Data	Poster	gVBvtRqU1_	This work presents OVIR-3D, a straightforward yet effective method for open-vocabulary 3D object instance retrieval without using any 3D data for training. Given a language query, the proposed method is able to return a ranked set of 3D object instance segments based on the feature similarity of the instance and the text query. This is achieved by a multi-view fusion of text-aligned 2D region proposals into 3D space, where the 2D region proposal network could leverage 2D datasets, which are more accessible and typically larger than 3D datasets. The proposed fusion process is efficient as it can be performed in real-time for most indoor 3D scenes and does not require additional training in 3D space. Experiments on public datasets and a real robot show the effectiveness of the method and its potential for applications in robot navigation and manipulation.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	lu23a	0	OVIR-3D: Open-Vocabulary 3D Instance Retrieval Without Training on 3D Data	1610	1620	1610-1620	1610	false	Lu, Shiyang and Chang, Haonan and Jing, Eric Pu and Boularias, Abdeslam and Bekris, Kostas	given family Shiyang Lu given family Haonan Chang given family Eric Pu Jing given family Abdeslam Boularias given family Kostas Bekris	2023-12-02		Proceedings of The 7th Conference on Robot Learning	229	inproceedings	date-parts 2023 12 2	https://proceedings.mlr.press/v229/lu23a/lu23a.pdf