仅用语义标签实现无需训练的激光雷达实例分割，性能超越多数主流方法。
Is clustering enough for LiDAR instance segmentation? A state-of-the-art training-free baseline
- 基于语义标签构建实例，无需任何标注或模型训练。
- 在SemanticKITTI和nuScenes上超越多数监督方法，实时运行于单线程CPU。
- 完全可解释，适合追求高效、无标注的自动驾驶场景理解应用。
激光雷达点云的全景分割是户外场景理解的基础，尤其在自动驾驶中具有关键作用。当前主流方法依赖端到端深度学习架构及大量人工标注的实例信息，但大规模点云数据集的标注成本与耗时仍是该领域的瓶颈。本文证明，仅使用语义标签即可实现具备竞争力的全景分割，实例预测无需任何训练或实例标注。所提方法在SemanticKITTI和nuScenes等标准基准上表现优于多数最先进监督方法，在SemanticKITTI上作为即插即用的实例头替换方案超越所有公开方法，且可在单线程CPU上实时运行，无需学习或参数调优。该方法完全可解释。Alpine结合最先进的语义分割模型，位居SemanticKITTI官方全景分割排行榜首位。代码已开源：https://github.com/valeoai/Alpine/
原文摘要 · Abstract (English)
Panoptic segmentation of LiDAR point clouds is fundamental to outdoor scene understanding, with autonomous driving being a primary application. While state-of-the-art approaches typically rely on end-to-end deep learning architectures and extensive manual annotations of instances, the significant cost and time investment required for labeling large-scale point cloud datasets remains a major bottleneck in this field. In this work, we demonstrate that competitive panoptic segmentation can be achieved using only semantic labels, with instances predicted without any training or annotations. Our method outperforms {most} state-of-the-art supervised methods on standard benchmarks including SemanticKITTI and nuScenes, and outperforms every publicly available method on SemanticKITTI as a drop-in instance head replacement, while running in real-time on a single-threaded CPU and requiring no instance labels. It is fully explainable, and requires no learning or parameter tuning. Alpine combined with state-of-the-art semantic segmentation ranks first on the official panoptic segmentation leaderboard of SemanticKITTI. Code is available at https://github.com/valeoai/Alpine/
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