3D Shape Segmentation Algorithm Using Weighted Energy Adaptive Distribution

In this paper, a fully supervised segmentation algorithm is proposed by using weighted energy adaptive distribution(WEAD). Firstly, the 3 D shapes are divided into several small patches using an over-segmentation method. Secondly, feature vectors of the patches are extracted as the training input and WEADs are used as the training output to re-weight the labels of segmentation. Finally, a corresponding deep neural network is trained. For an unlabeled 3 D shape, our algorithm automatically segments it by using the trained deep neural network followed by the graph cuts method. Extensive experimental results show that the mean square error(MSE) in the training process can be greatly reduced by using WEAD in our algorithm. And our method performs better than other fully supervised and unsupervised algorithms on the Princeton Segmentation Benchmark(PSB) dataset.