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

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.