ZS-SBPRnet: A Zero-Shot Sketch-Based Point Cloud Retrieval Network Based on Feature Projection and Cross-Reconstruction

This is the authors' abstract. We don't add key points for this paper.

With the widespread deployment of 3D sensors, point cloud analysis has become an important topic in the field of industrial information. This article proposes a novel zero-shot sketch-based point cloud retrieval network based on feature projection and cross reconstruction, termed as ZS-SBPRnet. As far as we know, the proposed ZS-SBPRnet is the first attempt at retrieving point clouds based on sketches under the zero-shot scenario. To tackle the problem of the cross-modal differences, a structure-preserving learnable feature projection module is designed to obtain view feature representations from point cloud features containing spatial structure information through feature projection. Besides, to achieve efficient cross-modal feature alignment under the zero-shot scenario, a sketch-point cloud cross-reconstruction mechanism is presented to promote cross-modal feature alignment between sketches and point clouds in visual space. Experimental results on the benchmark datasets validate the superiority of the proposed ZS-SBPRnet.