| Summary: | We consider the recent challenges of 3D shape analysis based on a volumetric CNN that requires a huge computational power. This high-cost approach forces to reduce the volume resolutions when applying 3D CNN on volumetric data. In this context, we propose a multiorientation volumetric deep neural network (MV-DNN) for 3D object classification with octree generating low-cost volumetric features. In comparison to conventional octree representations, we propose to limit the octree partition to a certain depth to reserve all leaf octants with sparsity features. This allows for improved learning of complex 3D features and increased prediction of object labels at both low and high resolutions. Our auxiliary learning approach predicts object classes based on the subvolume parts of a 3D object that improve the classification accuracy compared to other existing 3D volumetric CNN methods. In addition, the influence of views and depths of the 3D model on the classification performance is investigated through extensive experiments applied to the ModelNet40 database. Our deep learning framework runs significantly faster and consumes less memory than full voxel representations and demonstrate the effectiveness of our octree-based auxiliary learning approach for exploring high resolution 3D models. Experimental results reveal the superiority of our MV-DNN that achieves better classification accuracy compared to state-of-art methods on two public databases.
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