CLiNet: Joint Detection of Road Network Centerlines in 2D and 3D

• URL: http://arxiv.org/abs/2302.02259v1
• Date: Sat, 4 Feb 2023 23:30:04 GMT
• Title: CLiNet: Joint Detection of Road Network Centerlines in 2D and 3D
• Authors: David Paz, Srinidhi Kalgundi Srinivas, Yunchao Yao, and Henrik I. Christensen
• Abstract summary: This work introduces a new approach for joint detection of centerlines based on image data by localizing the features jointly in 2D and 3D. To develop and evaluate our approach, a large urban driving dataset dubbed AV Breadcrumbs is automatically labeled by leveraging vector map representations and projective geometry to annotate over 900,000 images.
• Score: 5.543544712471748
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This work introduces a new approach for joint detection of centerlines based on image data by localizing the features jointly in 2D and 3D. In contrast to existing work that focuses on detection of visual cues, we explore feature extraction methods that are directly amenable to the urban driving task. To develop and evaluate our approach, a large urban driving dataset dubbed AV Breadcrumbs is automatically labeled by leveraging vector map representations and projective geometry to annotate over 900,000 images. Our results demonstrate potential for dynamic scene modeling across various urban driving scenarios. Our model achieves an F1 score of 0.684 and an average normalized depth error of 2.083. The code and data annotations are publicly available.

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