Existing point cloud learning methods aggregate features from neighbouring
points relying on constructing graph in the spatial domain, which results in
feature update for each point based on spatially-fixed neighbours throughout
layers. In this paper, we propose a dynamic feature aggregation (DFA) method
that can transfer information by constructing local graphs in the feature
domain without spatial constraints. By finding k-nearest neighbors in the
feature domain, we perform relative position encoding and semantic feature
encoding to explore latent position and feature similarity information,
respectively, so that rich local features can be learned. At the same time, we
also learn low-dimensional global features from the original point cloud for
enhancing feature representation. Between DFA layers, we dynamically update the
constructed local graph structure, so that we can learn richer information,
which greatly improves adaptability and efficiency. We demonstrate the
superiority of our method by conducting extensive experiments on point cloud
classification and segmentation tasks. Implementation code is available:
this https URL

本文提出了一种动态特征聚合 (DFA) 方法，通过在特征域内构建局部图，进行 k - 最近邻探索潜在位置和特征相似性信息，结合原始点云低维全局特征表达，以增强特征表示，从而有效地进行点云分类。