  The repairing work of terracotta warriors in Emperor Qinshihuang Mausoleum
Site Museum is handcrafted by experts, and the increasing amounts of unearthed
pieces of terracotta warriors make the archaeologists too challenging to
conduct the restoration of terracotta warriors efficiently. We hope to segment
the 3D point cloud data of the terracotta warriors automatically and store the
fragment data in the database to assist the archaeologists in matching the
actual fragments with the ones in the database, which could result in higher
repairing efficiency of terracotta warriors. Moreover, the existing 3D neural
network research is mainly focusing on supervised classification, clustering,
unsupervised representation, and reconstruction. There are few pieces of
researches concentrating on unsupervised point cloud part segmentation. In this
paper, we present SRG-Net for 3D point clouds of terracotta warriors to address
these problems. Firstly, we adopt a customized seed-region-growing algorithm to
segment the point cloud coarsely. Then we present a supervised segmentation and
unsupervised reconstruction networks to learn the characteristics of 3D point
clouds. Finally, we combine the SRG algorithm with our improved CNN using a
refinement method. This pipeline is called SRG-Net, which aims at conducting
segmentation tasks on the terracotta warriors. Our proposed SRG-Net is
evaluated on the terracotta warriors data and ShapeNet dataset by measuring the
accuracy and the latency. The experimental results show that our SRG-Net
outperforms the state-of-the-art methods. Our code is shown in Code File
1~\cite{Srgnet_2021}.
