  We introduce Position Adaptive Convolution (PAConv), a generic convolution
operation for 3D point cloud processing. The key of PAConv is to construct the
convolution kernel by dynamically assembling basic weight matrices stored in
Weight Bank, where the coefficients of these weight matrices are
self-adaptively learned from point positions through ScoreNet. In this way, the
kernel is built in a data-driven manner, endowing PAConv with more flexibility
than 2D convolutions to better handle the irregular and unordered point cloud
data. Besides, the complexity of the learning process is reduced by combining
weight matrices instead of brutally predicting kernels from point positions.
  Furthermore, different from the existing point convolution operators whose
network architectures are often heavily engineered, we integrate our PAConv
into classical MLP-based point cloud pipelines without changing network
configurations. Even built on simple networks, our method still approaches or
even surpasses the state-of-the-art models, and significantly improves baseline
performance on both classification and segmentation tasks, yet with decent
efficiency. Thorough ablation studies and visualizations are provided to
understand PAConv. Code is released on https://github.com/CVMI-Lab/PAConv.
