EfficientLPS: Efficient LiDAR Panoptic Segmentation

Panoptic segmentation of point clouds is a crucial task that enables autonomous vehicles to comprehend their vicinity using their highly accurate and reliable LiDAR sensors. Existing top-down approaches tackle this problem by either combining independent task-specific networks or translating methods from the image domain ignoring the intricacies of LiDAR data and thus often resulting in suboptimal performance. In this article, we present the novel top-down efficient LiDAR panoptic segmentation (EfficientLPS) architecture that addresses multiple challenges in segmenting LiDAR point clouds, including distance-dependent sparsity, severe occlusions, large scale-variations, and reprojection errors. EfficientLPS comprises of a novel shared backbone that encodes with strengthened geometric transformation modeling capacity and aggregates semantically rich range-aware multiscale features. It incorporates new scale-invariant semantic and instance segmentation heads along with the panoptic fusion module which is supervised by our proposed panoptic periphery loss function. Additionally, we formulate a regularized pseudolabeling framework to further improve the performance of EfficientLPS by training on unlabeled data. We benchmark our proposed model on two large-scale LiDAR datasets: nuScenes, for which we also provide ground truth annotations, and SemanticKITTI. Notably, EfficientLPS sets the new state-of-the-art on both these datasets.

Automated summary

EfficientLPS is a novel architecture that addresses multiple challenges in segmenting LiDAR point clouds, including sparsity, occlusions, scale-variations, and reprojection errors. It comprises a shared backbone, new panoptic fusion module, and is supervised by the panoptic periphery loss function.