用实例掩码和四向标签提升道路中心线预测精度。
TopoMaskV2: Enhanced Instance-Mask-Based Formulation for the Road Topology Problem
- 采用掩码+注意力机制的新型道路中心线建模方法。
- 在OpenLane-V2上，A/B子集指标分别提升至49.4和51.8。
- 适合自动驾驶中道路拓扑理解任务的研究者参考。
近期，由于在解决道路拓扑问题上的优势，中心线已成为车道的流行表示形式。为提升中心线预测性能，我们提出一种名为TopoMask的新方法。与依赖关键点或参数化方法的以往工作不同，TopoMask采用基于实例掩码的建模方式，并结合基于掩码注意力的Transformer架构。我们引入四向标签表示，以增强掩码实例中的流向信息，并设计了相应的后处理技术实现掩码到中心线的转换。此外，我们证明了实例掩码建模可为参数化贝塞尔回归提供互补信息，融合两者输出能显著提升检测与拓扑性能。同时，我们分析了Lift Splat技术中柱状假设的局限性，并采用多高度分箱配置进行改进。实验结果表明，TopoMask在OpenLane-V2数据集上达到当前最优表现，其中Subset-A从44.1提升至49.4，Subset-B从44.7提升至51.8（基于V1.1 OLS基线）。
原文摘要 · Abstract (English)
Recently, the centerline has become a popular representation of lanes due to its advantages in solving the road topology problem. To enhance centerline prediction, we have developed a new approach called TopoMask. Unlike previous methods that rely on keypoints or parametric methods, TopoMask utilizes an instance-mask-based formulation coupled with a masked-attention-based transformer architecture. We introduce a quad-direction label representation to enrich the mask instances with flow information and design a corresponding post-processing technique for mask-to-centerline conversion. Additionally, we demonstrate that the instance-mask formulation provides complementary information to parametric Bezier regressions, and fusing both outputs leads to improved detection and topology performance. Moreover, we analyze the shortcomings of the pillar assumption in the Lift Splat technique and adapt a multi-height bin configuration. Experimental results show that TopoMask achieves state-of-the-art performance in the OpenLane-V2 dataset, increasing from 44.1 to 49.4 for Subset-A and 44.7 to 51.8 for Subset-B in the V1.1 OLS baseline.
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