  Voxel-based 3D object classification has been thoroughly studied in recent
years. Most previous methods convert the classic 2D convolution into a 3D form
that will be further applied to objects with binary voxel representation for
classification. However, the binary voxel representation is not very effective
for 3D convolution in many cases. In this paper, we propose a hybrid cascade
architecture for voxel-based 3D object classification. It consists of three
stages composed of fully connected and convolutional layers, dealing with easy,
moderate, and hard 3D models respectively. Both accuracy and speed can be
balanced in our proposed method. By giving each voxel a signed distance value,
an obvious gain regarding the accuracy can be observed. Besides, the mean
inference time can be speeded up hugely compared with the state-of-the-art
point cloud and voxel based methods.
