How well do saliency-based features perform for shape retrieval?

Sparse features have been successfully used in shape retrieval, by encoding feature descriptors into global shape signatures. We investigate how sparse features based on saliency models affect retrieval and provide recommendations on good saliency models for shape retrieval. Our results show that randomly selecting points on the surface produces better retrieval performance than using any of the evaluated salient keypoint detection, including ground-truth. We discuss the reasons for and implications of this unexpected result. Graphical abstractDisplay Omitted HighlightsAn evaluation of keypoint detectors on their performance in shape retrieval based on selected saliency models, including ground-truth.Sparse random points outperform human-selected salient points for shape retrieval on a generic dataset of watertight meshes.Restricting random points to non-salient regions causes a small decrease in retrieval performance.