融合几何特征与学习传播，提升点云配准鲁棒性
LoGDesc: Local geometric features aggregation for robust point cloud registration
- 结合PCA提取平面度、各向异性等局部几何特征
- 在噪声和低重叠场景下，配准精度显著优于传统方法
- 适合处理含噪、部分重叠的工业或扫描点云
本文提出一种新型混合描述子LoGDesc，用于3D点匹配与点云配准。该方法首先通过主成分分析（PCA）计算每个点的平面度、各向异性和全变差等几何先验信息；随后基于三角形构建邻域，利用法向量估计补充描述；最终通过局部图卷积与注意力机制在点之间传播特征。该特征提取器在ModelNet40、Bunny Stanford、KITTI及MVP（Multi-View Partial）-RG数据集上评估，尤其在噪声大、重叠率低的条件下表现优异。
原文摘要 · Abstract (English)
This paper introduces a new hybrid descriptor for 3D point matching and point cloud registration, combining local geometrical properties and learning-based feature propagation for each point's neighborhood structure description. The proposed architecture first extracts prior geometrical information by computing each point's planarity, anisotropy, and omnivariance using a Principal Components Analysis (PCA). This prior information is completed by a descriptor based on the normal vectors estimated thanks to constructing a neighborhood based on triangles. The final geometrical descriptor is propagated between the points using local graph convolutions and attention mechanisms. The new feature extractor is evaluated on ModelNet40, Bunny Stanford dataset, KITTI and MVP (Multi-View Partial)-RG for point cloud registration and shows interesting results, particularly on noisy and low overlapping point clouds.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。