计算机科学 ›› 2025, Vol. 52 ›› Issue (5): 199-211.doi: 10.11896/jsjkx.240400172
孔煜1, 熊风光1,2,3, 张志强1, 申超凡1, 胡明月1
KONG Yu1, XIONG Fengguang1,2,3, ZHANG Zhiqiang1, SHEN Chaofan1, HU Mingyue1
摘要: 针对特征提取阶段忽视局部几何嵌入的融合,特征交互阶段低重叠点云对之间的位置感知信息呈现弱相关性导致难以提取更富有表现力的特征,以及对应生成阶段出现部分错误对应导致求解的变换矩阵存在偏差等问题,提出了一种基于深度位置感知Transformer(DeepPAT)的三维点云低重叠配准方法。首先,设计了融合局部几何信息的局部特征提取网络,用于提取点云多层次特征;然后,设计了基于深度位置感知的Transformer(PAT)模块,通过学习点云自身和跨帧的几何和深度空间位置信息,提取低重叠率的源点云和目标点云的相关特征和重叠信息,以便进行低重叠特征匹配;最后,设计了由特征相似性项调节的极大团算法来减轻长度一致性所带来的空间模糊性,从而过滤离群点。其可作为一种即插即用的估计模块代替RANSAC等传统鲁棒估计器。在室内3DMatch数据集和合成ModelNet数据集上进行评估,实验结果表明:在测试ModelNet数据集的旋转和平移均方根误差方面,DeepPAT分别将误差降低至3.994和0.005;在测试3DMatch和3DLoMatch基准的配准召回率方面,DeepPAT分别比现有方法高出至少4.3%和3.6%。
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