  Point encoder is of vital importance for point cloud recognition. As the very
beginning step of whole model pipeline, adding features from diverse sources
and providing stronger feature encoding mechanism would provide better input
for downstream modules. In our work, we proposed a novel PeP module to tackle
above issue. PeP contains two main parts, a refined point painting method and a
LM-based point encoder. Experiments results on the nuScenes and KITTI datasets
validate the superior performance of our PeP. The advantages leads to strong
performance on both semantic segmentation and object detection, in both lidar
and multi-modal settings. Notably, our PeP module is model agnostic and
plug-and-play. Our code will be publicly available soon.
