Abstract

Partial retrieval is a long-standing problem in the 3D Object Retrieval community. Its main difficulties arise from how to define 3D local descriptors in a way that makes them effective for partial retrieval and robust to common real-world issues, such as occlusion, noise, or clutter, when dealing with 3D data. This SHREC track is based on the newly proposed ShapeBench benchmark to evaluate the matching performance of local descriptors. We propose an experiment consisting of three increasing levels of difficulty, where we combine different filters to simulate real-world issues related to the partial retrieval task. Our main findings show that classic 3D local descriptors like Spin Image are robust to several of the tested filters (and their combinations), but more recent learned local descriptors like GeDI can be competitive for some specific filters. Finally, no 3D local descriptor was able to successfully handle the hardest level of difficulty. © 2025 The Authors