Octree-based Convolutional Autoencoder Extreme Learning Machine for 3D Shape Classification

摘要

We introduce Octree-based Convolutional Autoencoder Extreme Learning Machine (OCA-ELM) for 3D shape classification. This approach combines Convolutional Autoencoder Extreme Learning Machine (CAE-ELM) with octreebased convolution to generate feature maps from several types of geometric data, and extract discriminative features with Extreme Learning Machine Autoencoder (ELM-AE). The extracted features can then be used for various computer graphics applications, such as 3D shape classification. Compared with other 3D classification methods, the proposed OCA-ELM has superior classification performance. Experiments on ModelNet40 show that OCA-ELM outperforms state-of-the-art CNN-based methods and surpasses CAE-ELM in classification accuracy by 3.69%, demonstrating the effectiveness of our method.