Multi-view recursive gated convolutions for 3D object recognition and retrieval

Abstract

Multi-view-based 3D shape recognition methods perform 3D object recognition and retrieval by processing series of images from various angles to generate a compact 3D descriptor. However, existing approaches often focus on integrating information from multiple views without addressing spatial interactions and redundancy when similar views are used. To overcome these challenges, we propose a novel framework, Multi-view Recursive Gated Convolutions (MVRGC). Our method first extracts features from multiple views at different scales, allowing for initial interaction of information across these views. Recursive gated convolutions are then applied to capture deeper spatial reciprocity and fine-tune feature interactions among views. Additionally, a preferred view module is introduced to reduce view redundancy by favoring distinctive and representative views. This module selects a subset of views that best describe the object while minimizing overlap. Experimental results on shape benchmark datasets demonstrate that MVRGC outperforms existing methods in 3D object recognition and retrieval tasks.