  Performances on standard 3D point cloud benchmarks have plateaued, resulting
in oversized models and complex network design to make a fractional
improvement. We present an alternative to enhance existing deep neural networks
without any redesigning or extra parameters, termed as Spatial-Neighbor Adapter
(SN-Adapter). Building on any trained 3D network, we utilize its learned
encoding capability to extract features of the training dataset and summarize
them as prototypical spatial knowledge. For a test point cloud, the SN-Adapter
retrieves k nearest neighbors (k-NN) from the pre-constructed spatial
prototypes and linearly interpolates the k-NN prediction with that of the
original 3D network. By providing complementary characteristics, the proposed
SN-Adapter serves as a plug-and-play module to economically improve performance
in a non-parametric manner. More importantly, our SN-Adapter can be effectively
generalized to various 3D tasks, including shape classification, part
segmentation, and 3D object detection, demonstrating its superiority and
robustness. We hope our approach could show a new perspective for point cloud
analysis and facilitate future research.
