  Fine-grained geometry, captured by aggregation of point features in local
regions, is crucial for object recognition and scene understanding in point
clouds. Nevertheless, existing preeminent point cloud backbones usually
incorporate max/average pooling for local feature aggregation, which largely
ignores points' positional distribution, leading to inadequate assembling of
fine-grained structures. To mitigate this bottleneck, we present an efficient
alternative to max pooling, Position Adaptive Pooling (PAPooling), that
explicitly models spatial relations among local points using a novel graph
representation, and aggregates features in a position adaptive manner, enabling
position-sensitive representation of aggregated features. Specifically,
PAPooling consists of two key steps, Graph Construction and Feature
Aggregation, respectively in charge of constructing a graph with edges linking
the center point with every neighboring point in a local region to map their
relative positional information to channel-wise attentive weights, and
adaptively aggregating local point features based on the generated weights
through Graph Convolution Network (GCN). PAPooling is simple yet effective, and
flexible enough to be ready to use for different popular backbones like
PointNet++ and DGCNN, as a plug-andplay operator. Extensive experiments on
various tasks ranging from 3D shape classification, part segmentation to scene
segmentation well demonstrate that PAPooling can significantly improve
predictive accuracy, while with minimal extra computational overhead. Code will
be released.
