3D lane detection from monocular images is a fundamental yet challenging task
in autonomous driving. Recent advances primarily rely on structural 3D
surrogates (e.g., bird's eye view) built from front-view image features and
camera parameters. However, the depth ambiguity in monocular images inevitably
causes misalignment between the constructed surrogate feature map and the
original image, posing a great challenge for accurate lane detection. To
address the above issue, we present a novel LATR model, an end-to-end 3D lane
detector that uses 3D-aware front-view features without transformed view
representation. Specifically, LATR detects 3D lanes via cross-attention based
on query and key-value pairs, constructed using our lane-aware query generator
and dynamic 3D ground positional embedding. On the one hand, each query is
generated based on 2D lane-aware features and adopts a hybrid embedding to
enhance lane information. On the other hand, 3D space information is injected
as positional embedding from an iteratively-updated 3D ground plane. LATR
outperforms previous state-of-the-art methods on both synthetic Apollo,
realistic OpenLane and ONCE-3DLanes by large margins (e.g., 11.4 gain in terms
of F1 score on OpenLane). Code will be released at
https://github.com/JMoonr/LATR .Comment: Accepted by ICCV2023 (Oral