RoadPainter: Points Are Ideal Navigators for Topology transformER
This addresses precise road understanding for autonomous driving systems, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles road scene topology reasoning for autonomous systems by presenting RoadPainter, which detects and reasons lane centerline topology from multi-view images using point extraction from masks, achieving state-of-the-art performance on the OpenLane-V2 dataset.
Topology reasoning aims to provide a precise understanding of road scenes, enabling autonomous systems to identify safe and efficient routes. In this paper, we present RoadPainter, an innovative approach for detecting and reasoning the topology of lane centerlines using multi-view images. The core concept behind RoadPainter is to extract a set of points from each centerline mask to improve the accuracy of centerline prediction. We start by implementing a transformer decoder that integrates a hybrid attention mechanism and a real-virtual separation strategy to predict coarse lane centerlines and establish topological associations. Then, we generate centerline instance masks guided by the centerline points from the transformer decoder. Moreover, we derive an additional set of points from each mask and combine them with previously detected centerline points for further refinement. Additionally, we introduce an optional module that incorporates a Standard Definition (SD) map to further optimize centerline detection and enhance topological reasoning performance. Experimental evaluations on the OpenLane-V2 dataset demonstrate the state-of-the-art performance of RoadPainter.