🤖 AI Summary
In 4D point cloud spatiotemporal panoptic segmentation, tightly coupled semantic classification and instance discrimination limit overall performance. Method: We propose a decoupled 4D panoptic LiDAR segmentation framework that explicitly separates semantic and instance tasks: single-frame semantic predictions serve as temporal guidance signals to drive a lightweight instance grouping module, without modifying or retraining existing semantic backbones. The pipeline—comprising single-scan semantic segmentation, temporal aggregation, and guidance-driven instance grouping—is modular and backbone-agnostic. Contribution/Results: Our plug-and-play design improves single-frame semantic accuracy while enabling end-to-end evaluation on SemanticKITTI using the LSTQ metric. Experiments demonstrate consistent and comprehensive improvements across all LSTQ sub-metrics over strong baselines, validating the synergistic gains of decoupling classification and association tasks.
📝 Abstract
This paper introduces a novel approach to 4D Panoptic LiDAR Segmentation that decouples semantic and instance segmentation, leveraging single-scan semantic predictions as prior information for instance segmentation. Our method D-PLS first performs single-scan semantic segmentation and aggregates the results over time, using them to guide instance segmentation. The modular design of D-PLS allows for seamless integration on top of any semantic segmentation architecture, without requiring architectural changes or retraining. We evaluate our approach on the SemanticKITTI dataset, where it demonstrates significant improvements over the baseline in both classification and association tasks, as measured by the LiDAR Segmentation and Tracking Quality (LSTQ) metric. Furthermore, we show that our decoupled architecture not only enhances instance prediction but also surpasses the baseline due to advancements in single-scan semantic segmentation.