3DSSR: 3D Subscene Retrieval
Published:
Reza Asad , Manolis Savva . IEEE Computer Vision and Pattern Recognition. CVPR 2023 Workshop on Structural and Compositional Learning on 3D Data (Spotlight Presentation).
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.