TL;DR
TopoHR introduces a hierarchical, end-to-end framework for cyclic topology reasoning in driving scenes, integrating point-to-instance relationships to improve accuracy and robustness.
Contribution
It proposes a novel hierarchical centerline representation and topology reasoning module that iteratively enhance each other for better topology understanding.
Findings
Achieves state-of-the-art results on OpenLane-V2 benchmark.
Significant improvements in detection and topology metrics over previous methods.
Validates the effectiveness of hierarchical and cyclic interaction components.
Abstract
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, they often neglect the importance of \textit{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…
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