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.

翻译：点云全景分割是一项具有挑战性的任务，旨在为语义分割和实例分割提供统一解决方案，以预测连贯点的分组。以往的方法将语义分割和实例分割视为代理任务，在实例分割任务中，它们要么使用聚类方法，要么使用边界框来收集实例分组，但计算成本高昂且需要手工设计。在本文中，我们提出了一种简单而有效的点云统一全景分割（PUPS）框架，该框架使用一组点级分类器，以端到端的方式直接预测语义和实例分组。为实现PUPS，我们在训练流程中引入了二分匹配，使得分类器能够专门预测实例分组，从而摆脱了手工设计（例如锚框和非极大值抑制）。为获得更好的分组结果，我们利用Transformer解码器对点分类器进行迭代优化，并开发了一种基于上下文的CutMix数据增强方法以克服类别不平衡问题。最终，PUPS在SemanticKITTI全景分割任务的排行榜上夺得第一，并在nuScenes数据集上取得了最先进的性能。