Camera Self-Calibration: Deep Learning from Driving Scenes

Engineering notes

Key topics: autonomous driving system, autonomous driving, perception. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Prior to driving, cameras embedded in an autonomous driving system need to be calibrated intrinsically. Calibration is crucial to ensure that safety-related perception functions can reliably perceive the environment. Vehicle cameras are also exposed to mechanical perturbations requiring periodic re-calibration with regular uses. The current widely-accepted calibration approaches are based on robust but potentially demanding target-based methods. Such methods require a car to be taken offline and rely on static infrastructure and operators. Targetless online calibration approaches exist but remain largely unadopted due to the accuracy gaps compared to the classical methods. We propose a deep-learning-based self-calibration strategy for the vehicular camera that learns from driving scenes—they make an inherently large-scale dataset—and is validated back-to-back against checkerboard reprojection error. Our approach results in a 2.5% decrease in subpixel reprojection error compared to the existing deep-learning-based approaches. We also demonstrate its practical application in the automotive domain.

Links and sources

• Official / arXiv page

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