Geometry-aware Temporal Aggregation Network for Monocular 3D Lane Detection

• URL: http://arxiv.org/abs/2504.20525v1
• Date: Tue, 29 Apr 2025 08:10:17 GMT
• Title: Geometry-aware Temporal Aggregation Network for Monocular 3D Lane Detection
• Authors: Huan Zheng, Wencheng Han, Tianyi Yan, Cheng-zhong Xu, Jianbing Shen,
• Abstract summary: We propose a novel Geometry-aware Temporal Aggregation Network (GTA-Net) for monocular 3D lane detection.<n>On one hand, we develop the Temporal Geometry Enhancement Module (TGEM), which exploits geometric consistency across successive frames.<n>On the other hand, we present the Temporal Instance-aware Query Generation (TIQG), which strategically incorporates temporal cues into query generation.
• Score: 62.27919334393825
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Monocular 3D lane detection aims to estimate 3D position of lanes from frontal-view (FV) images. However, current monocular 3D lane detection methods suffer from two limitations, including inaccurate geometric information of the predicted 3D lanes and difficulties in maintaining lane integrity. To address these issues, we seek to fully exploit the potential of multiple input frames. First, we aim at enhancing the ability to perceive the geometry of scenes by leveraging temporal geometric consistency. Second, we strive to improve the integrity of lanes by revealing more instance information from temporal sequences. Therefore, we propose a novel Geometry-aware Temporal Aggregation Network (GTA-Net) for monocular 3D lane detection. On one hand, we develop the Temporal Geometry Enhancement Module (TGEM), which exploits geometric consistency across successive frames, facilitating effective geometry perception. On the other hand, we present the Temporal Instance-aware Query Generation (TIQG), which strategically incorporates temporal cues into query generation, thereby enabling the exploration of comprehensive instance information. Experiments demonstrate that our GTA-Net achieves SoTA results, surpassing existing monocular 3D lane detection solutions.

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