Abstract
3D single object tracking (3DSOT) in LiDAR point clouds is a critical task for outdoor perception, enabling real-time perception of object location, orientation, and motion. Despite the impressive performance of current 3DSOT methods, evaluating them on clean datasets inadequately reflects their comprehensive performance, as the adverse weather conditions in real-world surroundings has not been considered. One of the main obstacles is the lack of adverse weather benchmarks for the evaluation of 3DSOT. To this end, this work proposes a challenging benchmark for LiDAR-based 3DSOT in adverse weather, which comprises two synthetic datasets (KITTI-A and nuScenes-A) and one real-world dataset (CADC-SOT) spanning three weather types: rain, fog, and snow. Based on this benchmark, five representative 3D trackers from different tracking frameworks conducted robustness evaluation, resulting in significant performance degradations. This prompts the question: What are the factors that cause current advanced methods to fail on such adverse weather samples? Consequently, we explore the impacts of adverse weather and answer the above question from three perspectives: 1) target distance; 2) template shape corruption; and 3) target shape corruption. Finally, based on domain randomization and contrastive learning, we designed a dual-branch tracking framework for adverse weather, named DRCT, achieving excellent performance in benchmarks.
Abstract (translated)
基于激光雷达点云的单个物体三维跟踪（3DSOT）是室外感知的关键任务，它能够实现目标对象位置、姿态和运动的实时感知。尽管目前的3DSOT方法表现出色，但仅在清洁数据集上进行评估无法全面反映其性能，因为现实世界中的恶劣天气条件未被充分考虑。其中一个主要障碍是没有为3DSOT评估设计的恶劣天气基准测试。 为此，这项工作提出了一个具有挑战性的基于激光雷达的3DSOT恶劣天气基准测试，包括两个合成数据集（KITTI-A和nuScenes-A）以及一个真实世界的数据集（CADC-SOT），涵盖了雨、雾和雪三种类型的恶劣天气。根据这一基准，来自不同跟踪框架的五个代表性的3D追踪器进行了鲁棒性评估，结果表明性能显著下降。这引发了问题：是什么因素导致当前先进的方法在这种恶劣天气样本上表现不佳？因此，我们从三个方面探讨了恶劣天气的影响，并回答了上述问题：1）目标距离；2）模板形状损坏；和3）目标形状损坏。 最后，在领域随机化和对比学习的基础上，我们设计了一个用于恶劣天气的双分支跟踪框架DRCT（Domain Randomization and Contrastive Learning-based Dual-branch Tracker），在基准测试中取得了卓越的成绩。
URL
https://arxiv.org/abs/2501.07133
