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Point encoder is of vital importance for point cloud recognition. As the verybeginning step of whole model pipeline, adding features from diverse sourcesand providing stronger feature encoding mechanism would provide better inputfor downstream modules. In our work, we proposed a novel PeP module to tackleabove issue. PeP contains two main parts, a refined point painting method and aLM-based point encoder. Experiments results on the nuScenes and KITTI datasetsvalidate the superior performance of our PeP. The advantages leads to strongperformance on both semantic segmentation and object detection, in both lidarand multi-modal settings. Notably, our PeP module is model agnostic andplug-and-play. Our code will be publicly available soon.

PeP: a Point enhanced Painting method for unified point cloud tasks