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收稿：2023-12-29，
修回：2024-04-03，
纸质出版：2024-12-16
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目的
2
针对目前基于深度学习的低重叠度点云配准方法在学习全局点云场景后进行特征匹配时，忽略局部特征间作用的问题，提出了一种结合边缘特征增强的层次注意力点云配准方法。
方法
2
首先，利用边缘自适应核点卷积（edge adaptive kernel point convolution， EAKPConv）模块提取源点云、目标点云特征，增强边缘特征识别能力。然后，利用局部空间差异注意模块（local spatial contrast attention module， LSCAM）聚合局部空间差异捕捉点云的几何细节，利用序列相似度关联模块（sequential similarity association module， SSAM）计算量化两点云间的相似分数，并利用相似分数引导局部匹配。最后，通过LSCAM模块与SSAM模块结合的层次化注意力融合模块（hierarchical attention fusion module， HAFM）整合局部、全局特征，实现全局匹配。
结果
2
在室内场景点云配准数据集3DMatch和三维模型数据集ModelNet-40上进行了对比实验，本算法在3DMatch和3DLoMatch上的配准召回率分别达到93.2%和67.3%；在ModelNet-40和ModelLoNet-40上取得了最低的旋转误差（分别为1.417和3.141）以及平移误差（分别为0.013 91和0.072）。此外，本文算法在推理效率上比REGTR算法减少了10 ms左右。
结论
2
本文算法通过自底向上的层次化处理方式显著提升了有限重叠场景点云的配准精度，同时降低了推理时间。
Objective
2
Low overlap point cloud registration presents a significant obstacle in the realm of computer vision， specifically in the context of deep-learning-based approaches. After acquiring knowledge from global point cloud scenes for feature matching， these deep-learning-based methods often fail to consider the interactions among local features， thus greatly impeding the efficiency of registration in settings where local feature interactions are vital for establishing precise alignment. The intricate interplay among local characteristics， which is crucial for accurately identifying and aligning partially intersecting point clouds， is also inadequately represented. This lack of consideration not only affects the reliability of point cloud registration in situations with limited overlap but also restricts the use of deep learning methods in varied and intricate settings. Therefore， techniques that include the comprehension of local feature interactions into the deep learning framework are crucial for point cloud registration， especially in situations with limited overlap.
Method
2
The present study introduces a novel technique for aligning point clouds with low overlap. This technique uses the edge adaptive KPConv （EAKPConv） module to enhance the identification of edge characteristics. The integration of local and global features is effectively accomplished by the combination of the hierarchical attention fusion module （HAFM） and the local spatial comparison attention module （LSCAM）. LSCAM exploits the capacity of the attention mechanism to consolidate information， thus enabling the model to prioritize those connections with target nodes and to position itself near the clustered center of mass. In this way， the model can flexibly capture complex details of the point cloud. The SSAM system utilizes a hierarchical architecture， in which each tier of local matching modules applies its own similarity metric to quantify the similarities among point clouds. The local features are subsequently modified and transmitted to the subsequent layer of attention modules to establish a hierarchical structure. This structure also allows the model to collect and merge the inputs from local matches at different scales and levels of complexity， thereby forming global feature correspondences. In this model， the multilayer perceptron （MLP） is used to accurately find the ideal correspondences and successfully complete the alignment procedure.
Result
2
This work provides empirical evidence supporting the improved efficacy of the proposed algorithm as demonstrated by its consistent performance across multiple datasets. Notably， this algorithm achieved impressive registration recall rates of 93.2% and 67.3% on the 3DMatch and 3DLoMatch datasets， respectively. In the experimental evaluation conducted on the ModelNet-40 and ModelLoNet-40 datasets， this algorithm achieved minimal rotational errors of 1.417 degrees and 3.141 degrees， respectively， and recorded translational errors of 0.013 91 and 0.072. These outcomes highlight the effectiveness of this algorithm in point cloud registration and demonstrate its capability to accurately align point clouds with low rotational and translational discrepancies. These results also point to a significant enhancement in the accuracy of the proposed algorithm compared with the REGTR approach. Specifically， in contrast to REGTR， the proposed algorithm achieved significantly reduced inference times of 27.205 ms and 30.991 ms on the 3DMatch and ModelNet-40 datasets， respectively. The findings of this study emphasize the performance of the proposed algorithm in effectively addressing the challenging issue of disregarding features in point cloud registration tasks with minimal overlap.
Conclusion
2
This article presents a novel point cloud matching technique that combines edge improvement with hierarchical attention. This technique integrates polynomial kernel functions into the EAKPConv framework to improve the identification of edge features in point clouds and uses HAFM to extract specific local information. The module improves feature matching by using the similarities in edge features. This approach successfully achieves a harmonious combination of local and global feature matching， hence enhancing the comprehension of point cloud data. Implementing a hierarchical analysis technique greatly increases the registration accuracy by accurately matching local-global information. Furthermore， increasing the cross-entropy loss function enhances the accuracy of local matching and reduces misalignments. This study assesses the performance of the proposed algorithm on the ModelNet-40， ModelelloNet-40， 3DMatch， and 3DLoMatch datasets， and results indicate that this algorithm substantially enhances registration accuracy， particularly in difficult situations with limited data overlap. This algorithm also exhibits superior registration efficiency compared with standard approaches.
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