  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.
