论文标题
Hausdorff点卷积与几何先验
Hausdorff Point Convolution with Geometric Priors
论文作者
论文摘要
没有形状感知的响应，很难用紧凑的核有效地表征点云的3D几何形状。在本文中，我们主张使用Hausdorff距离作为计算点卷积响应的形状感知距离度量。我们提出的技术，即Hausdorff点卷积（HPC），是形状感知的。我们表明，HPC构成了一个强大的点特征学习，只有四种类型的几何先验作为内核。我们进一步开发了基于HPC的深神经网络（HPC-DNN）。可以通过调整网络权重来结合输入和内核点集之间的最短距离来实现特定于任务的学习。我们还通过设计用于多尺度功能编码的多内核HPC来实现层次功能学习。广泛的实验表明，HPC-DNN优于强点卷积基线（例如KPCONV），在S3DIS上实现了2.8％的MIOU性能增强，Semantickitti的语义分段任务上的Semantickitti上的性能提高了1.5％。
Without a shape-aware response, it is hard to characterize the 3D geometry of a point cloud efficiently with a compact set of kernels. In this paper, we advocate the use of Hausdorff distance as a shape-aware distance measure for calculating point convolutional responses. The technique we present, coined Hausdorff Point Convolution (HPC), is shape-aware. We show that HPC constitutes a powerful point feature learning with a rather compact set of only four types of geometric priors as kernels. We further develop a HPC-based deep neural network (HPC-DNN). Task-specific learning can be achieved by tuning the network weights for combining the shortest distances between input and kernel point sets. We also realize hierarchical feature learning by designing a multi-kernel HPC for multi-scale feature encoding. Extensive experiments demonstrate that HPC-DNN outperforms strong point convolution baselines (e.g., KPConv), achieving 2.8% mIoU performance boost on S3DIS and 1.5% on SemanticKITTI for semantic segmentation task.
