= 256$. In the experiment, we train our network on a NVIDIA GTX 1080Ti GPU using ADAM optimizer with the initial learning rate of 0.0001 and batch size of 8. The learning rate is decreased by 0.3 for every 20 epochs.

Parameters
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All experiments on parameter comparison are evaluated under ModelNet10. ModelNet10 contains 4899 CAD models from 10 categories and is split into 3991 for training and 908 for testing. For each model, we adopt 1024 points which are uniformly sampled from mesh faces and are normalized into a unit ball before being fed into the network. During the training process, the loss function keeps decreasing and stabilizes around the 180th epoch. To acquire the accuracies on ModelNet10, we train a linear SVM from the global features obtained by the auto-encoder. Specifically, the OneVsRest strategy is adopted with the linearSVM function as the kernel.

We first explore the number of sampled points $M$ which determines the distribution of local regions inside point clouds. In the experiment, we keep the network settings as depicted in the network configuration and vary the number of sampled points $M$ from 128 to 320.

     $M$      128     192       256       320   
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