论文标题
全景phnet：通过聚类伪热图迈向实时和高精度LiDar Panoptic分割
Panoptic-PHNet: Towards Real-Time and High-Precision LiDAR Panoptic Segmentation via Clustering Pseudo Heatmap
论文作者
论文摘要
作为一项不断上升的任务，泛型分割面临着语义细分和实例分割的挑战。但是，就速度和准确性而言，现有的LIDAR方法仍然受到限制。在本文中，我们提出了一个快速，高性能的基于激光雷达的框架，称为全盘式纯手册，具有三个有吸引力的方面：1）我们介绍了一个集群的伪热图作为一个新范式，随后是中心分组模块，在无需对象学习任务的情况下，该中心分组模块产生了实例组中的中心。 2）提出了一个KNN转换器模块，以建模前景点之间的相互作用，以确保偏移回归。 3）对于骨干设计，我们将融合细粒度的体素特征和2D鸟的眼景（BEV）功能，并具有不同的接收场，以同时使用详细信息和全球信息。 Semantickitti数据集和Nuscenes数据集的广泛实验表明，我们的Panoptic-Phnet通过以实时速度显着的边距超过了最先进的方法。我们在Semantickitti的公共排行榜上获得了第一名，并在最近发布的Nuscenes排行榜上获得了领先的表现。
As a rising task, panoptic segmentation is faced with challenges in both semantic segmentation and instance segmentation. However, in terms of speed and accuracy, existing LiDAR methods in the field are still limited. In this paper, we propose a fast and high-performance LiDAR-based framework, referred to as Panoptic-PHNet, with three attractive aspects: 1) We introduce a clustering pseudo heatmap as a new paradigm, which, followed by a center grouping module, yields instance centers for efficient clustering without object-level learning tasks. 2) A knn-transformer module is proposed to model the interaction among foreground points for accurate offset regression. 3) For backbone design, we fuse the fine-grained voxel features and the 2D Bird's Eye View (BEV) features with different receptive fields to utilize both detailed and global information. Extensive experiments on both SemanticKITTI dataset and nuScenes dataset show that our Panoptic-PHNet surpasses state-of-the-art methods by remarkable margins with a real-time speed. We achieve the 1st place on the public leaderboard of SemanticKITTI and leading performance on the recently released leaderboard of nuScenes.
