计算机科学 ›› 2022, Vol. 49 ›› Issue (10): 207-213.doi: 10.11896/jsjkx.210900066
刘娜丽1,3, 田彦1,3, 宋亚东4, 江腾飞3, 王勋1,2, 杨柏林1
LIU Na-li1,3, TIAN Yan1,3, SONG Ya-dong4, JIANG Teng-fei3, WANG Xun1,2, YANG Bai-lin1
摘要： 3D形变技术是计算机图形学领域的研究热点之一。当前的3D形变方法主要通过聚合局部相邻的体素特征来学习物体形变前后的变化,未充分挖掘非局部体素特征之间的相互关系,这种环境信息的缺失导致模型无法捕获更具辨识性的特征。针对上述问题,设计了一种基于环境信息挖掘的体素形变网络,该网络能够同时对局部和环境信息进行提取,从不同的空间域中挖掘环境信息以提升网络的表征性能,进而建模物体形变前后的变化关系。引入自注意力机制,通过学习特征空间中不同体素的非局部依赖性,以提升体素特征的辨别力;引入一种多尺度分析方法,使用不同扩张率的空洞卷积分别提取不同感知域中的环境信息,为模型提供了更丰富的上下文特征。此外,文中分析了特征融合对模型的影响,并设计了一种基于编码器-解码器特征融合方法,自适应地对编码器和解码器提取的特征进行融合,提高了模型的非线性映射能力。在自建的齿科数据集上进行了充分的对比实验,结果表明,与现有方法相比,所提方法在形变预测任务的准确率上有一定的提升。
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