VFMVAC: View-filtering-based multi-view aggregating convolution for 3D shape recognition and retrieval

Abstract

Multi-view based 3D shape recognition methods have achieved state-of-the-art performance in 3D shape recognition and retrieval. The main focus of multi-view based approaches is determining how to fuse multi-view features into a compact, descriptive, and robust 3D shape descriptor that can then be utilized for 3D shape recognition and retrieval. This paper proposes a novel multi-view aggregating framework, view-filtering-based multi-view aggregating convolution (VFMVAC) to learn global shape descriptors for 3D shape recognition. The proposed VFMVAC applies a voting-based view filtering strategy to select representative views, also introduces a novel multi-view aggregating module to integrate multi-view features; this substantially improves the descriptiveness of the descriptors, and therefore improves the performance of 3D shape recognition and retrieval. Specifically, all views are fed into a voting-based view filtering module to select the top-k representative views. Subsequently, the features of the top-k views are fed into the multi-view aggregating module, which first conducts cross-view channel shuffle for achieving cross-view information flowing, and the resulted reshaped features are then fed into the aggregating convolution module for feature fusion. Experiments on benchmark datasets demonstrate that the proposed VFMVAC is effective and outperforms several recent techniques with respect to the classification and retrieval performance, robustness and efficiency.