TopoHR：面向驾驶场景循环拓扑推理的分层中心线表示与点-实例关系
TopoHR: Hierarchical Centerline Representation for Cyclic Topology Reasoning in Driving Scenes with Point-to-Instance Relations
TopoHR 提出分层中心线表示（点查询、实例查询、语义表示）与分层拓扑推理模块，在统一架构中同时建模点-实例（P2I）和实例-实例（I2I）关系，并让中心线检测与拓扑推理形成循环增强。对做自动驾驶感知的研究者，这套 P2I 关系建模和端到端循环交互设计可直接迁移到车道线拓扑、路网结构理解等任务。
原文依据 we design a hierarchical topology reasoning module that captures both fine-grained P2I relationships and global instance-to-instance (I2I) connections
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论拓扑推理对自动驾驶至关重要。现有方法主要聚焦于中心线检测的实例级学习，随后依赖简化的 MLP 层构成顺序模块进行拓扑推理。此外，这些方法往往忽略了点-实例（P2I）关系在拓扑推理中的重要性。为解决上述局限，我们提出 TopoHR（拓扑分层表示），一种新颖的端到端框架，在中心线检测与拓扑推理之间建立循环交互，使二者能够迭代地相互增强。具体而言，我们引入一种分层中心线表示，包含点查询、实例查询和语义表示。这些多级特征在分层中心线解码器中被无缝集成与融合。此外，我们设计了一个分层拓扑推理模块，在统一架构中同时捕获细粒度的 P2I 关系和全局的实例-实例（I2I）连接。借助这些新颖组件，TopoHR 确保了准确且鲁棒的拓扑推理。在 OpenLane-V2 基准上，TopoHR 以显著提升刷新了最优性能。值得注意的是，与先前最佳结果相比，TopoHR 在 subset_A 上实现了 DET_l +3.8、TOP_ll +5.4 的提升，在 subset_B 上实现了 DET_l +11.0、TOP_ll +7.9 的提升，验证了所提出组件的有效性。代码将在论文录用后公开。
英文原文摘要 1572 字符
Topology reasoning is crucial for autonomous driving. Current methods primarily focus on instance-level learning for centerline detection, followed by a sequential module for topology reasoning that relies on simplified MLP layers. Moreover, these approaches often neglect the importance of point-to-instance (P2I) relationships in topology reasoning. To address these limitations, we present TopoHR (Topological Hierarchical Representation), a novel end-to-end framework that establishes cyclic interaction between centerline detection and topology reasoning, allowing them to iteratively enhance each other. Specifically, we introduce a hierarchical centerline representation including point queries, instance queries, and semantic representations. These multi-level features are seamlessly integrated and fused within a hierarchical centerline decoder. Furthermore, we design a hierarchical topology reasoning module that captures both fine-grained P2I relationships and global instance-to-instance (I2I) connections within a unified architecture. With these novel components, TopoHR ensures accurate and robust topology reasoning. On the OpenLane-V2 benchmark, TopoHR refreshes state-of-the-art performance with significant improvements. Notably, compared with previous best results, TopoHR achieves +3.8 in \mathrm DET _ \text l , +5.4 in \mathrm TOP _ \text ll on subset_A and +11.0 in \mathrm DET _ \text l , +7.9 in \mathrm TOP _ \text ll on subset_B, validating the effectiveness of the proposed components. The code will be shared publicly upon paper acceptance.