A variational approach for estimation of monocular depth and camera motion in autonomous driving

Engineering notes

Key topics: autonomous driving, depth estimation, monocular depth. 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

In this paper, a new direct computational approach to dense 3D reconstruction in autonomous driving is proposed to simultaneously estimate the depth and the camera motion for the motion stereo problem. A traditional Structure from Motion framework is utilized to establish geometric constrains for our variational model. The architecture is mainly composed of the texture constancy constraint, one-order motion smoothness constraint, a second-order depth regularize constraint and a soft constraint. The texture constancy constraint can improve the robustness against illumination changes. One-order motion smoothness constraint can reduce the noise in estimation of dense correspondence. The depth regularize constraint is used to handle inherent ambiguities and guarantee a smooth or piecewise smooth surface, and the soft constraint can provide a dense correspondence as initial estimation of the camera matrix to improve the robustness future. Compared to the traditional dense Structure from Motion approaches and popular stereo approaches, our monocular depth estimation results are more accurate and more robust. Even in contrast to the popular depth from single image networks, our variational approach still has good performance in estimation of monocular depth and camera motion.

Links and sources

• Official / arXiv page
• PDF from original source

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