Self-Supervised Segmentation for Terracotta Warrior Point Cloud (EGG-Net)

Abstract

At present, our team focuses on cultural relics restoration and fragment splicing research. In the research process of terracotta warrior splicing, we find that the existing calibrated fragment data is relatively small, which is not enough for related research. Therefore, we need to calibrate and segment different parts of the intact terracotta warrior data and extract some data we need to use in the future. However, at present, we are short of human resources. If we want to carry out manual calibration, it will take much time, bringing trouble to our future work. Therefore, we hope to design a method to automatically calibrate the terracotta warrior dataset with a small amount of calibrated data. The existing 3D neural network research mainly focuses on supervised classification, segmentation, and unsupervised reconstruction. We cannot find enough schemes to refer to, and the existing methods do not perform well on our terracotta warrior dataset. Therefore, in this article, we propose EGG-Net to solve this problem. EGG-Net is an end-to-end self-supervised model, and it consists of two modules. The first module is an encoder based on dynamic graph and edge convolution. We can extract point cloud features with this module. The second module, called segmenter, is based on multi-layer perceptron, adding labels to points and segmenting the point cloud. After the neural network, we add point refinement operation to the pipeline. Point refinement can adjust the cluster label estimated by the neural network with superpoint, which can optimize the loss function and help us train the neural network. Our EGG-Net can back-propagate with the refinement operation. We evaluated EGG-Net on the terracotta warrior data and ShapeNet Part by measuring the accuracy and the latency. The experiment result shows that our EGG-Net outperforms the state-of-the-art methods.