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

arXiv 2107.13167 ↗First posted 28 Jul 2021cs.CVcited by 0

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Abstract (arXiv)

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~citeSrgnet2021.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 40 references that are in the map. First posted on arXiv 28 Jul 2021.

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References · 20

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8 more
  1. 2017Deep Projective 3D Semantic Segmentationcited by 81
  2. 2019Multi-view PointNet for 3D Scene Understandingcited by 76
  3. 2016SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentationcited by 70
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  7. 2020PointHop++: A Lightweight Learning Model on Point Sets for 3D Classificationcited by 22
  8. 2019Normal Estimation for 3D Point Clouds via Local Plane Constraint and Multi-scale Selectioncited by 20

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