  Discrete point cloud objects lack sufficient shape descriptors of 3D
geometries. In this paper, we present a novel method for aggregating
hypothetical curves in point clouds. Sequences of connected points (curves) are
initially grouped by taking guided walks in the point clouds, and then
subsequently aggregated back to augment their point-wise features. We provide
an effective implementation of the proposed aggregation strategy including a
novel curve grouping operator followed by a curve aggregation operator. Our
method was benchmarked on several point cloud analysis tasks where we achieved
the state-of-the-art classification accuracy of 94.2% on the ModelNet40
classification task, instance IoU of 86.8 on the ShapeNetPart segmentation
task, and cosine error of 0.11 on the ModelNet40 normal estimation task.
