论文标题
FKACONV：点云卷积的功能内核对齐
FKAConv: Feature-Kernel Alignment for Point Cloud Convolution
论文作者
论文摘要
点云处理的最新最新方法基于点卷积的概念，为此提出了几种方法。在本文中，受图像处理中的离散卷积的启发，我们提供了一种公式来关联和分析许多点卷积方法。我们还提出了自己的卷积变体，该变体分开了无几何核重量的估计及其对特征空间支持的一致性。此外，我们定义了卷积的点采样策略，该策略既有效又快速。最后，使用我们的卷积和抽样策略，我们在时间和记忆效率的同时，在分类和语义分割基准方面显示了竞争成果。
Recent state-of-the-art methods for point cloud processing are based on the notion of point convolution, for which several approaches have been proposed. In this paper, inspired by discrete convolution in image processing, we provide a formulation to relate and analyze a number of point convolution methods. We also propose our own convolution variant, that separates the estimation of geometry-less kernel weights and their alignment to the spatial support of features. Additionally, we define a point sampling strategy for convolution that is both effective and fast. Finally, using our convolution and sampling strategy, we show competitive results on classification and semantic segmentation benchmarks while being time and memory efficient.
