In this paper, we present a seed-region-growing CNN(SRG-Net) for unsupervised part segmentation with 3D point clouds of terracotta warriors. Previous neural network researches in 3D are mainly about supervised classification, clustering, unsupervised representation and reconstruction. There are few researches focusing on unsupervised point cloud part segmentation. To address these problems, we present a seed-region-growing CNN(SRG-Net) for unsupervised part segmentation with 3D point clouds of terracotta warriors. Firstly, we propose our customized seed region growing algorithm to coarsely segment the point cloud. Then we present our supervised segmentation and unsupervised reconstruction networks to better understand the characteristics of 3D point clouds. Finally, we combine the SRG algorithm with our improved CNN using a refinement method called SRG-Net to conduct the segmentation tasks on the terracotta warriors. Our proposed SRG-Net are evaluated on the terracotta warriors data and the benchmark dataset of ShapeNet with measuring mean intersection over union(mIoU) and latency. The experimental results show that our SRG-Net outperforms the state-of-the-art methods. Our code is available at https://github.com/hyoau/SRG-Net.
翻译:在本文中,我们展示了一个种子区域增长的CNN(SRG-Net),用于使用3D点云进行不受监督的部分分割。首先,我们建议用我们定制的种子区域增长算法来粗略地分割点云层。然后,我们展示我们监督的分割和不受监督的重建网络,以更好地了解3D点云的特性。最后,我们用一种称为SRG-Net的改良方法,将SRG算法与改进的CNN结合起来,以进行有关Terracotta勇士的分解任务。我们提议的SRG-Net正在用地球科塔战士的3D点云进行评估。首先,我们建议我们定制的种子区域增长算法用来测量点云层的粗略部分。然后,我们展示我们的受监督的分解和不受监督的重建网络,以便更好地了解3D点云层云的特性。最后,我们将SRG算法与我们改进的CNNM结合起来,使用SRG-Net 来进行分解任务。我们提议的SRG-Net-ROG-ROD的实验结果显示我们在SphyO-ROG-RD的系统。