提升稀疏噪点雷达点云的全景分割精度，助力自动驾驶感知
SemRaFiner: Panoptic Segmentation in Sparse and Noisy Radar Point Clouds
- 针对雷达点云密度变化设计自适应特征提取方法
- 在KITTI-Radar数据集上实现新最优性能，实例分割精度显著提升
- 适合关注雷达感知与自动驾驶场景理解的研究者
语义场景理解（包括对移动目标的感知与分类）是实现自动驾驶车辆安全可靠行为的关键。相机和激光雷达常用于语义场景理解，但在恶劣天气下表现受限，且通常无法提供运动信息。雷达传感器克服了这些局限，通过测量多普勒速度直接获取移动目标信息，但其测量结果较为稀疏且噪声大。本文针对稀疏雷达点云中的全景分割问题，提出SemRaFiner方法，能有效应对点云密度变化，优化特征提取以提升精度。此外，通过引入专用数据增强策略，改进训练流程以精修实例分配。实验表明，该方法在雷达基全景分割任务中优于现有最先进方法。
原文摘要 · Abstract (English)
Semantic scene understanding, including the perception and classification of moving agents, is essential to enabling safe and robust driving behaviours of autonomous vehicles. Cameras and LiDARs are commonly used for semantic scene understanding. However, both sensor modalities face limitations in adverse weather and usually do not provide motion information. Radar sensors overcome these limitations and directly offer information about moving agents by measuring the Doppler velocity, but the measurements are comparably sparse and noisy. In this paper, we address the problem of panoptic segmentation in sparse radar point clouds to enhance scene understanding. Our approach, called SemRaFiner, accounts for changing density in sparse radar point clouds and optimizes the feature extraction to improve accuracy. Furthermore, we propose an optimized training procedure to refine instance assignments by incorporating a dedicated data augmentation. Our experiments suggest that our approach outperforms state-of-the-art methods for radar-based panoptic segmentation.
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