计算机科学 ›› 2021, Vol. 48 ›› Issue (4): 192-196.doi: 10.11896/jsjkx.200100048
赵新灿, 常寒星, 金仁标
ZHAO Xin-can, CHANG Han-xing, JIN Ren-biao
摘要: 在真实的扫描环境中,由于视线遮挡或技术人员操作不当,实际采集到的点云模型会存在形状不完整的问题。点云模型的不完整性会对后续应用产生严重的影响,因此提出3D点云形状补全GAN用于完成点云模型的形状补全。该网络的点云重建部分由PointNet中用于数据对齐的T-Net结构与3D点云AutoEncoder网络相结合,来完成预测和填充缺失数据,识别器采用3D点云AutoEncoder中的Encoder部分对补全3D点云数据与真实的3D点云数据进行区分。最后,在ShapeNet数据集中训练上述网络结构,对所训练的网络模型进行验证并与其他基准方法进行定性比较。从实验结果可以看出,3D点云形状补全GAN可以将具有缺失数据的点云模型补全为完整的3D点云。在ShapeNet的3个子数据集chair,table以及bed上,相比基于3D点云AutoEncoder的方法,所提方法的F1分数分别提高了3.0%,3.3%以及3.1%,相比基于体素3D-EPN的方法,所提方法的F1分数分别提高了9.9%,5.8%以及4.3%。
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