Training-Free Global Geometric Association for 4D LiDAR Panoptic Segmentation

ArXi:2512.18991v2 Announce Type: replace-cross Dominant paradigms for 4D LiDAR panoptic segmentation are usually required to train deep neural networks with large superimposed point clouds or design dedicated modules for instance association. However, these approaches perform redundant point processing and consequently become computationally expensive, yet still overlook the rich geometric priors inherently provided by raw point clouds. To this end, we