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.

在本文中，提出了一种新的自动驾驶中密集 3D 重建的直接计算方法，以同时估计运动立体问题的深度和相机运动。Motion 框架中的传统结构用于为我们的变分模型建立几何约束。该架构主要由纹理恒常性约束、一阶运动平滑约束、二阶深度正则化约束和软约束组成。纹理恒常性约束可以提高对光照变化的鲁棒性。一阶运动平滑约束可以减少密集对应估计中的噪声。深度正则化约束用于处理固有的歧义并保证平滑或分段平滑的表面，并且软约束可以提供密集对应作为相机矩阵的初始估计，以提高未来的鲁棒性。与传统的 Motion 方法和流行的立体方法中的密集结构相比，我们的单目深度估计结果更准确、更稳健。即使与来自单图像网络的流行深度相比，我们的变分方法在估计单眼深度和相机运动方面仍然具有良好的性能。