3DSSR: 3D Subscene Retrieval

Reza Asad, Manolis Savva; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023, pp. 2708-2716

Abstract


We present the task of 3D subscene retrieval (3DSSR). In this task, a user specifies a query object and a set of context objects in a 3D scene. Then, a system retrieves and ranks subscenes from a database of 3D scenes that best correspond to the configuration defined by the query. This formulation generalizes prior work on context-based 3D object retrieval and 3D scene retrieval. To tackle this task we present PointCrop: a self-supervised point cloud encoder training scheme that enables retrieval of geometrically similar subscenes without relying on object category supervision. We evaluate PointCrop against alternative methods and baselines through a suite of evaluation metrics that measure the degree of subscene correspondence. Our experiments show that PointCrop training outperforms supervised and prior self-supervised training paradigms by 4.33% and 9.11% in mAP respectively.

Related Material


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[bibtex]
@InProceedings{Asad_2023_CVPR, author = {Asad, Reza and Savva, Manolis}, title = {3DSSR: 3D Subscene Retrieval}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {2708-2716} }