3D Feature Learning via Convolutional Auto-Encoder Extreme Learning Machine

3D shape features play a crucial role in graphics applications like 3D shape matching, recognition, and retrieval. Traditional 3D descriptors are hand-crafted features which are labor-intensively designed and are unable to extract discriminative information from existing large-scale 3D data. Convolutional neuron networks and auto-encoders are two most popular neuron networks in the field of deep learning. Based on the framework of extreme learning machines, we propose a rapid 3D feature learning method —convolutional extreme learning machine auto-encoder, which could automatically learn shape features from 3D shape dataset. Our method runs faster than existing deep learning methods by approximately two orders of magnitude. Experiments show that our method is superior to traditional machine learning methods based on hand-crafted features and other deep learning methods in tasks of 3D shape classification and 3D object detection.