Abstract: A dual-branch, multi-level feature fusion network (DMFNet) was proposed to address point cloud registration under partial overlap, occlusion, and noise. In the encoder, a rotation branch and a translation branch were configured in parallel. Self-attention fusion and cross-attention fusion modules were inserted at shallow, middle, and deep layers so that multi-scale interaction between source and reference point clouds was achieved. A rotation–translation feature fusion module was designed to strengthen pose estimation. A lightweight Set Transformer was adopted as the regressor; multi-layer induced-attention blocks and an attention-pooling module were used to directly regress the quaternion and the translation vector. DMFNet does not rely on overlap-region detection or explicit mask estimation, and it shows strong adaptability and generalization. Comparative experiments with six registration methods were conducted on ModelNet40, and a generalization study was carried out on the Stanford 3D Scanning dataset. The results show that, under noisy conditions on the ModelNet40 dataset, DMFNet reduces RMSE(t) and Error(R) by 21.32% and 14.47% compared with MAC, and demonstrates superior robustness and registration accuracy.

摘要： 针对点云配准中因部分重叠、遮挡与噪声干扰导致的配准精度差与鲁棒性不足问题，提出了一种双分支结构下多层次特征融合的点云配准网络(DMFNet)。网络在编码阶段并行设置旋转分支与平移分支，在浅层、中层与深层分别引入自注意力融合与交叉注意力融合模块，实现源点云与参考点云之间的多尺度特征交互与深度融合，并设计旋转与平移特征融合模块以增强姿态估计能力。回归阶段基于轻量级Set Transformer回归器，采用多层诱导注意力块与注意力池化模块，直接回归四元数与平移向量。 DMFNet不依赖重叠区域的检测或显式掩码估计，具备更强的适应性与泛化能力。在ModelNet40数据集上与6种点云配准方法进行对比实验，在斯坦福3D扫描数据集进行泛化能力实验。实验结果表明，在ModelNet40数据集添加噪声的情况下，DMFNet相较于MAC的方法，在RMSE(t)和Error(R)指标上降低了21.32%和14.47%，展现出更优的鲁棒性与配准精度。