Depth Information Encoding in 2D Images via Projection of Multiple Parallel Laser Lines

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Depth Information Encoding in 2D Images via Projection of Multiple Parallel Laser Lines

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Author(s): Huimin Li ¹ ^,

Publication date (Electronic preprint): 27 March 2025

Journal: ScienceOpen Preprints

Publisher: ScienceOpen

Keywords: Multiple parallel laser lines, Depth estimation, LiDAR, Bipolar coordinate system, Autonomous driving, Machine learning, 3D Scene, Deterministic 3D Perception, 3D occupancy, Pure vision

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Abstract

This paper proposes a monocular 3D perception method based on the projection of multiple fixed-spacing parallel laser lines. By leveraging the inverse proportionality between the spacing of parallel laser stripes in the camera imaging plane and their distance from the sensor—a fundamental perspective property—the author achieves depth information embedding directly within RGB images. The ultra-narrow laser stripes enable efficient compression into 2D vector graphics, forming a static LiDAR (Light Detection and Ranging) architecture devoid of mechanical scanning components. Preliminary modeling confirms that laser stripes inherently encode scene contour features, allowing direct extraction of deterministic spatial parameters for drivable areas and obstacles, outperforming probabilistic 3D occupancy prediction techniques reliant on complex neural network inference. Further analysis demonstrates that the depth-encoded laser stripe features simplify training workflows for machine learning models, substantially reducing convolutional computation demands in feature extraction. The system employs a fully static optical design without moving parts or programmable components, complies with the Class 1 laser safety standard (IEC 60825-1:2014), and establishes a novel hardware-algorithm co-design paradigm for autonomous driving perception.

该文提出了一种基于多条固定间距平行激光线投影的单目 3D 感知方法。通过利用相机成像平面中平行激光条的间距与它们与传感器的距离之间的反比例性（一个基本的透视特性），作者实现了直接嵌入 RGB 图像中的深度信息。超窄激光条可以有效地压缩为 2D 矢量图形，形成一个没有机械扫描组件的静态 LiDAR（光检测和测距）架构。初步建模证实，激光条纹本身就编码了场景轮廓特征，允许直接提取可驾驶区域和障碍物的确定性空间参数，优于依赖于复杂神经网络推理的概率 3D 占用预测技术。进一步的分析表明，深度编码的激光条纹特征简化了机器学习模型的训练工作流程，大大减少了特征提取中的卷积计算需求。该系统采用全静态光学设计，无需移动部件或可编程组件，符合 1 类激光安全标准（IEC 60825-1：2014），并为自动驾驶感知建立了新颖的硬件算法协同设计范式。

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Author and article information

Journal

Title: ScienceOpen Preprints

Publisher: ScienceOpen

Publication date (Electronic preprint): 27 March 2025

Affiliations

[1 ] Zhuzhou Hybrid Co.,Ltd.;

Author notes

[* ]Email: lihuiminok@ 123456163.com .

Author information

Huimin Li https://orcid.org/0009-0007-7368-5070

Article

DOI: 10.14293/PR2199.001550.v1

SO-VID: 8d9d8fcb-b01f-4b21-abbc-e1d9f45e57c7

License:

This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

History

Date received : 27 March 2025

References

Li Huimin. Depth Information Encoding in 2D Images via Projection of Multiple Parallel Laser Lines. 2025. Zenodo. [Cross Ref]

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[1] Li Huimin. Depth Information Encoding in 2D Images via Projection of Multiple Parallel Laser Lines. 2025. Zenodo. [Cross Ref]

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