论文标题
点态：双重归一化是您要进行点云分析所需的全部
PointNorm: Dual Normalization is All You Need for Point Cloud Analysis
论文作者
论文摘要
由于点云数据结构的不规则性，点云分析是具有挑战性的。现有作品通常采用PointNet ++的临时采样组操作，然后采用复杂的本地和/或全局特征提取器来利用点云的3D几何形状。不幸的是，采样组操作并不能解决点云的不规则性，而复杂的局部和/或全球特征提取器导致计算效率不佳。在本文中，我们引入了一个新型的双向模块，在采样组操作后，以有效有效地解决了不规则性问题。 DualNorm模块由点归一化组成，该点标准化将分组的点标准化为采样点，并反向点归一化，从而将采样点标准化为分组点。拟议的框架（点态）利用本地平均值和全球标准偏差从本地和全球特征中受益，同时保持忠实的推理速度。实验表明，我们在ModelNet40分类，ScanObjectNN分类，ShapenetPart部件分割和S3DIS语义分割方面实现了出色的准确性和效率。代码可在https://github.com/shenzheng2000/pointnorm-for-point-cloud-analysis中找到。
Point cloud analysis is challenging due to the irregularity of the point cloud data structure. Existing works typically employ the ad-hoc sampling-grouping operation of PointNet++, followed by sophisticated local and/or global feature extractors for leveraging the 3D geometry of the point cloud. Unfortunately, the sampling-grouping operations do not address the point cloud's irregularity, whereas the intricate local and/or global feature extractors led to poor computational efficiency. In this paper, we introduce a novel DualNorm module after the sampling-grouping operation to effectively and efficiently address the irregularity issue. The DualNorm module consists of Point Normalization, which normalizes the grouped points to the sampled points, and Reverse Point Normalization, which normalizes the sampled points to the grouped points. The proposed framework, PointNorm, utilizes local mean and global standard deviation to benefit from both local and global features while maintaining a faithful inference speed. Experiments show that we achieved excellent accuracy and efficiency on ModelNet40 classification, ScanObjectNN classification, ShapeNetPart Part Segmentation, and S3DIS Semantic Segmentation. Code is available at https://github.com/ShenZheng2000/PointNorm-for-Point-Cloud-Analysis.
