Depth3DLane: Monocular 3D Lane Detection via Depth Prior Distillation

• URL: http://arxiv.org/abs/2504.18325v1
• Date: Fri, 25 Apr 2025 13:08:41 GMT
• Title: Depth3DLane: Monocular 3D Lane Detection via Depth Prior Distillation
• Authors: Dongxin Lyu, Han Huang, Cheng Tan, Zimu Li,
• Abstract summary: We introduce a BEV-based framework to address limitations and improve 3D lane detection accuracy.<n>We leverage Depth Prior Distillation to transfer semantic depth knowledge from a teacher model.<n>Our method achieves state-of-the-art performance in terms of z-axis error.
• Score: 5.909083729156255
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Monocular 3D lane detection is challenging due to the difficulty in capturing depth information from single-camera images. A common strategy involves transforming front-view (FV) images into bird's-eye-view (BEV) space through inverse perspective mapping (IPM), facilitating lane detection using BEV features. However, IPM's flat-ground assumption and loss of contextual information lead to inaccuracies in reconstructing 3D information, especially height. In this paper, we introduce a BEV-based framework to address these limitations and improve 3D lane detection accuracy. Our approach incorporates a Hierarchical Depth-Aware Head that provides multi-scale depth features, mitigating the flat-ground assumption by enhancing spatial awareness across varying depths. Additionally, we leverage Depth Prior Distillation to transfer semantic depth knowledge from a teacher model, capturing richer structural and contextual information for complex lane structures. To further refine lane continuity and ensure smooth lane reconstruction, we introduce a Conditional Random Field module that enforces spatial coherence in lane predictions. Extensive experiments validate that our method achieves state-of-the-art performance in terms of z-axis error and outperforms other methods in the field in overall performance. The code is released at: https://anonymous.4open.science/r/Depth3DLane-DCDD.

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