Beyond Point Anchors: Patch-Aware Rotary Encoding for Masked Point Cloud Representation Learning

Point cloud patch tokens summarize local neighborhoods, yet anchor-only rotary encoding derives positional phases solely from normalized anchor coordinates. We introduce Patch-Aware Rotary Encoding (PaRE), which incorporates local patch geometry into rotary positional interactions through additive phase residuals derived from measured anchor-to-centroid offsets. The resulting relative phases combine anchor displacement with differences in centroid offsets, complementing the geometry encoded in token content. PaRE preserves the original anchors, fixed frequencies, and anchor-based normalization, and uses shared, bounded, frequency-wise residual coefficients that add only 32 learnable parameters in our configuration. With point-only pre-training on ShapeNet in a masked latent prediction framework, PaRE achieves strong performance across diverse downstream classification and segmentation tasks, including 90.77% overall accuracy on ScanObjectNN's PB_T50_RS setting. These results demonstrate the effectiveness of patch-aware rotary interactions for learning transferable representations through masked point cloud pre-training. Code is provided in the appendix.