  Point cloud panoptic segmentation is a challenging task that seeks a holistic
solution for both semantic and instance segmentation to predict groupings of
coherent points. Previous approaches treat semantic and instance segmentation
as surrogate tasks, and they either use clustering methods or bounding boxes to
gather instance groupings with costly computation and hand-crafted designs in
the instance segmentation task. In this paper, we propose a simple but
effective point cloud unified panoptic segmentation (PUPS) framework, which use
a set of point-level classifiers to directly predict semantic and instance
groupings in an end-to-end manner. To realize PUPS, we introduce bipartite
matching to our training pipeline so that our classifiers are able to
exclusively predict groupings of instances, getting rid of hand-crafted
designs, e.g. anchors and Non-Maximum Suppression (NMS). In order to achieve
better grouping results, we utilize a transformer decoder to iteratively refine
the point classifiers and develop a context-aware CutMix augmentation to
overcome the class imbalance problem. As a result, PUPS achieves 1st place on
the leader board of SemanticKITTI panoptic segmentation task and
state-of-the-art results on nuScenes.
