  We present a novel attention-based mechanism to learn enhanced point features
for point cloud processing tasks, e.g., classification and segmentation. Unlike
prior works, which were trained to optimize the weights of a pre-selected set
of attention points, our approach learns to locate the best attention points to
maximize the performance of a specific task, e.g., point cloud classification.
Importantly, we advocate the use of single attention point to facilitate
semantic understanding in point feature learning. Specifically, we formulate a
new and simple convolution, which combines convolutional features from an input
point and its corresponding learned attention point, or LAP, for short. Our
attention mechanism can be easily incorporated into state-of-the-art point
cloud classification and segmentation networks. Extensive experiments on common
benchmarks such as ModelNet40, ShapeNetPart, and S3DIS all demonstrate that our
LAP-enabled networks consistently outperform the respective original networks,
as well as other competitive alternatives, which employ multiple attention
points, either pre-selected or learned under our LAP framework.
