RoadPainter: Points Are Ideal Navigators for Topology transformER

TL;DR

RoadPainter is a novel method that uses points and transformer-based reasoning to accurately detect and understand road lane topologies from multi-view images, improving autonomous navigation.

Contribution

It introduces a point-based topology reasoning approach with a transformer decoder and map integration, advancing lane centerline detection accuracy.

Findings

Achieves state-of-the-art results on OpenLane-V2 dataset.

Effectively integrates multi-view images for topology reasoning.

Enhances lane detection with optional SD map module.

Abstract

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.