Point Cloud Neural Networks (PCNNs) have emerged as a vital tool for latency-sensitive 3D perception applications, such as autonomous driving and AR/VR. However, their inherent computational redundancy—arising from excessive global sampling/search operations and repeated feature updates/aggregations caused by shared neighbors—severely constrains execution efficiency. More critically, conventional redundancy elimination methods usually introduce operations that are highly GPU-unfriendly, resulting in high memory overhead, increased branch divergence, irregular memory access, and serial dependencies, which together pose a significant challenge to PCNN acceleration.