Unsupervised Segmentation for Terracotta Warrior with Seed-Region-Growing CNN (SRG-Net)

TL;DR

This paper introduces SRG-Net, an unsupervised segmentation method combining seed-region-growing and CNN for 3D point cloud data of terracotta warriors, improving efficiency in archaeological restoration tasks.

Contribution

The paper presents a novel unsupervised segmentation pipeline for 3D point clouds, specifically tailored for terracotta warriors, integrating seed-region-growing with an improved CNN.

Findings

SRG-Net outperforms existing methods in accuracy

SRG-Net reduces segmentation latency

Effective on both terracotta warrior and ShapeNet datasets

Abstract

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}.