将语义与实例分割解耦，用时序语义先验提升4D激光雷达全景分割精度。
D-PLS: Decoupled Semantic Segmentation for 4D-Panoptic-LiDAR-Segmentation
- 分两步走：先单帧语义分割，再用时序聚合结果指导实例分割
- 在SemanticKITTI上，分类和关联任务的LSTQ指标均显著超越基线
- 模块化设计可无缝接入任意语义模型，无需重训练
本文提出一种新型4D全景激光雷达分割方法D-PLS，通过解耦语义与实例分割，利用单帧语义预测作为先验信息指导实例分割。D-PLS首先进行单帧语义分割，并对时序结果进行聚合，以此引导实例分割过程。其模块化设计可无缝集成于任意语义分割架构之上，无需修改网络结构或重新训练。我们在SemanticKITTI数据集上评估该方法，结果显示其在分类和关联任务上的表现均显著优于基线，以LiDAR分割与追踪质量（LSTQ）为衡量标准。此外，解耦架构不仅提升了实例预测性能，还因单帧语义分割能力的增强而带来整体优势。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。