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3DLaneFormer: Rethinking Learning Views for 3D Lane Detection

Citation Author(s):
Kun Dong, Jian Xue, Xing Lan, Ke Lu
Submitted by:
Kun Dong
Last updated:
14 June 2024 - 10:11am
Document Type:
Supplementary material

Accurate 3D lane detection from monocular images is crucial for autonomous driving. Recent advances leverage either front-view (FV) or bird’s-eye-view (BEV) features for prediction, inevitably limiting their ability to perceive driving environments precisely and resulting in suboptimal performance. To overcome the limitations of using features from a single view, we design a novel dual-view cross-attention mechanism, which leverages features from FV and BEV simultaneously. Based on this mechanism, we propose 3DLaneFormer, a powerful framework for 3D lane detection.
It outperforms the latest BEV-based or FV-based approaches through extensive experiments on challenging benchmarks and thus verifies the necessity and benefits of utilizing features in both views.

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