Unsupervised 3D shape segmentation and co segmentation via deep learning
In this paper, we propose a novel unsupervised algorithm for automatically segmenting a single 3D shape or co segmenting a family of 3D shapes using deep learning. The algorithm consists of three stages. In the first stage, we pre decompose each 3D shape of interest into primitive patches to generate over segmentation and compute various signatures as low level shape features. In the second stage, high level features are learned, in an unsupervised style, from the low level ones based on deep learning. Finally, either segmentation or co segmentation results can be quickly reported by patch clustering in the high level feature space. The experimental results on the Princeton Segmentation Benchmark and the Shape COSEG Dataset exhibit superior segmentation performance of the proposed method over the previous state of the art approaches. We propose a novel segmentation and co segmentation approach for 3D shapes.We introduce deep learning into 3D shape segmentation and co segmentation.Our method is data driven but does not need a tedious labeling process.Our algorithm achieves better or comparable performance when compared with the state of the art methods.
Comput. Aided Geom. Des.