计算机工程

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基于超点图网络的三维点云室内场景分割方法

  

  • 发布日期:2020-12-11

Three-dimensional Point Cloud Indoor Segmentation Model based on Super Point Graph Network

  • Published:2020-12-11

摘要: 三维点云语义分割在自动驾驶、室内导航等领域有着广泛应用。针对目前点云数据集样本不均衡和类 PointNet 网络 不能充分利用点云邻域信息问题,提出一种基于超点图网络(SPG)的三维点云场景分割方法。该方法通过最小割理论,根 据几何信息将原始点云块同质分割为超点。利用小型 PointNet,将点云原始特征映射到高维空间挖掘场景中深层语义信息。 为有效提高点云上下文语义分割,构建一种自归一化属性门控单元及采用二维图像领域针对样本不均衡的 Focal Loss 损失函 数来实现点云的场景分割。实验结果表明,该方法能够显著地提高三维点云场景语义分割的准确率和分割效果。在 S3DIS 数 据集上的平均交并比、总体精度和平均精度识别准确率分别为 63.8%、86.4%和 74.3%,较 SPG 模型分别提升 1.7%、0.9%和 1.3%。

Abstract: Three-dimensional(3D) semantic segmentation is widely applied in various fields such as automatic driving and indoor navigation. Aiming at the problems that the samples of current point cloud dataset are unbalanced and the similar pointnet network can not effectively utilize the neighborhood information around the point cloud.This paper proposes a 3D point cloud scene segmentation method based on Super Point Graph network. By using the min-cut theory, the point cloud of the scene is divided into several blocks homogeneously according to the geometric information. Each block is denoted as a super point.Utilizing the mini-pointnet, the original features of point cloud are mapped to high-dimensional features fully mining the deep fine-grained geometric features in scenes.By constructing a self-normalization Gate Recurrent Unit and the Focal Loss aimed to the imbalance samples, this model can effectively extract the context features of point cloud. The experimental results show that the proposed method can improve the accuracy and segmentation effect of 3D point cloud semantic segmentation effectively. The MIOU,OA and mAcc on S3DIS dataset reach 63.8%, 86.4% and 74.3% respectively, which have the increases of 1.7% , 0.9% and 1.3% compared with the SPG model.