  Existing point cloud feature learning networks often incorporate sequences of
sampling, neighborhood grouping, neighborhood-wise feature learning, and
feature aggregation to learn high-semantic point features that represent the
global context of a point cloud. Unfortunately, the compounded loss of
information concerning granularity and non-maximum point features due to
sampling and max pooling could adversely affect the high-semantic point
features from existing networks such that they are insufficient to represent
the local context of a point cloud, which in turn may hinder the network in
distinguishing fine shapes. To cope with this problem, we propose a novel point
cloud feature learning network, PointStack, using multi-resolution feature
learning and learnable pooling (LP). The multi-resolution feature learning is
realized by aggregating point features of various resolutions in the multiple
layers, so that the final point features contain both high-semantic and
high-resolution information. On the other hand, the LP is used as a generalized
pooling function that calculates the weighted sum of multi-resolution point
features through the attention mechanism with learnable queries, in order to
extract all possible information from all available point features.
Consequently, PointStack is capable of extracting high-semantic point features
with minimal loss of information concerning granularity and non-maximum point
features. Therefore, the final aggregated point features can effectively
represent both global and local contexts of a point cloud. In addition, both
the global structure and the local shape details of a point cloud can be well
comprehended by the network head, which enables PointStack to advance the
state-of-the-art of feature learning on point clouds. The codes are available
at https://github.com/kaist-avelab/PointStack.
