  Panoptic segmentation as an integrated task of both static environmental
understanding and dynamic object identification, has recently begun to receive
broad research interest. In this paper, we propose a new computationally
efficient LiDAR based panoptic segmentation framework, called GP-S3Net.
GP-S3Net is a proposal-free approach in which no object proposals are needed to
identify the objects in contrast to conventional two-stage panoptic systems,
where a detection network is incorporated for capturing instance information.
Our new design consists of a novel instance-level network to process the
semantic results by constructing a graph convolutional network to identify
objects (foreground), which later on are fused with the background classes.
Through the fine-grained clusters of the foreground objects from the semantic
segmentation backbone, over-segmentation priors are generated and subsequently
processed by 3D sparse convolution to embed each cluster. Each cluster is
treated as a node in the graph and its corresponding embedding is used as its
node feature. Then a GCNN predicts whether edges exist between each cluster
pair. We utilize the instance label to generate ground truth edge labels for
each constructed graph in order to supervise the learning. Extensive
experiments demonstrate that GP-S3Net outperforms the current state-of-the-art
approaches, by a significant margin across available datasets such as, nuScenes
and SemanticPOSS, ranking first on the competitive public SemanticKITTI
leaderboard upon publication.
