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

Highlights：

• We propose a high-precision 3D shape multi-view recognition framework which can highly promote the performance of the 3D shape classification and retrieval.

• A voting-based view filtering algorithm is proposed; this algorithm can select the most representative views among the existing views to represent 3D shapes, thereby significantly improving memory usage efficiency and reducing the computational cost.

• A novel multi-view aggregating module is designed; in particular, the k-view features are shuffled using a cross-view channel shuffle module that considers the combination of features across views, thereby allowing for their sufficient fusion; furthermore, this module fuses the multi-view features via an aggregating convolution and considers all features of each view, thereby avoiding information loss induced by the traditional pooling methods.

• The proposed framework achieves state-of-the-art recognition and retrieval performance on benchmark datasets.