Point Cloud

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• Graph Convolutional Network (GCN), Graph Neural Networks (Graph Nets), Geometric Deep Learning
• Vote3Deep
• Vote3Deep LIDAR
• SqueezeSeg
• PointNet
• Passenger Screening
• 3D Model

3DCNN

Schematic diagram of the Deep 3D Convolutional Neural Network and FEATURE-Softmax Classifier models. a Deep 3D Convolutional Neural Network. The feature extraction stage includes 3D convolutional and Pooling / Sub-sampling: Max, Mean layers. 3D filters in the 3D convolutional layers search for recurrent spatial patterns that best capture the local biochemical features to separate the 20 amino acid microenvironments. Pooling / Sub-sampling: Max, Mean layers perform down-sampling to the input to increase translational invariances of the network. By following the 3DCNN and 3D Pooling / Sub-sampling: Max, Mean layers with fully connected layers, the pooled filter responses of all filters across all positions in the protein box can be integrated. The integrated information is then fed to the Softmax classifier layer to calculate class probabilities and to make the final predictions. Prediction error drives parameter updates of the trainable parameters in the classifier, fully connected layers, and convolutional filters to learn the best feature for the optimal performances. b The FEATURE Softmax Classifier. The FEATURE Softmax model begins with an input layer, which takes in FEATURE vectors, followed by two fully-connected layers, and ends with a Softmax classifier layer. In this case, the input layer is equivalent to the feature extraction stage. In contrast to 3DCNN, the prediction error only drives parameter learning of the fully connected layers and classifier. The input feature is fixed during the whole training process