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Voxel-based 3D object classification has been thoroughly studied in recentyears. Most previous methods convert the classic 2D convolution into a 3D formthat will be further applied to objects with binary voxel representation forclassification. However, the binary voxel representation is not very effectivefor 3D convolution in many cases. In this paper, we propose a hybrid cascadearchitecture for voxel-based 3D object classification. It consists of threestages composed of fully connected and convolutional layers, dealing with easy,moderate, and hard 3D models respectively. Both accuracy and speed can bebalanced in our proposed method. By giving each voxel a signed distance value,an obvious gain regarding the accuracy can be observed. Besides, the meaninference time can be speeded up hugely compared with the state-of-the-artpoint cloud and voxel based methods.

A Fast Hybrid Cascade Network for Voxel-based 3D Object Classification