  Accurate 3D lane estimation is crucial for ensuring safety in autonomous
driving. However, prevailing monocular techniques suffer from depth loss and
lighting variations, hampering accurate 3D lane detection. In contrast, LiDAR
points offer geometric cues and enable precise localization. In this paper, we
present DV-3DLane, a novel end-to-end Dual-View multi-modal 3D Lane detection
framework that synergizes the strengths of both images and LiDAR points. We
propose to learn multi-modal features in dual-view spaces, i.e., perspective
view (PV) and bird's-eye-view (BEV), effectively leveraging the modal-specific
information. To achieve this, we introduce three designs: 1) A bidirectional
feature fusion strategy that integrates multi-modal features into each view
space, exploiting their unique strengths. 2) A unified query generation
approach that leverages lane-aware knowledge from both PV and BEV spaces to
generate queries. 3) A 3D dual-view deformable attention mechanism, which
aggregates discriminative features from both PV and BEV spaces into queries for
accurate 3D lane detection. Extensive experiments on the public benchmark,
OpenLane, demonstrate the efficacy and efficiency of DV-3DLane. It achieves
state-of-the-art performance, with a remarkable 11.2 gain in F1 score and a
substantial 53.5% reduction in errors. The code is available at
\url{https://github.com/JMoonr/dv-3dlane}.
