3D model classification using convolutional neural network
نویسنده
چکیده
Our goal is to classify 3D models directly using convolutional neural network. Most of existing approaches rely on a set of human-engineered features. We use 3D convolutional neural network to let the network learn the features over 3D space to minimize classification error. We trained and tested over ShapeNet dataset with data augmentation by applying random transformations. We made various visual analysis to find out what the network has learned. We extended our work to extract additional information such as pose of the 3D model.
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