3DSSR: 3D Subscene Retrieval

Reza Asad, Manolis Savva

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.