In autonomous driving, monocular sequences contain lots of information. Monocular depth estimation, camera ego-motion estimation and optical flow estimation in consecutive frames are high-profile concerns recently. By analyzing tasks above, pixels in the first frame are modeled into three parts: the rigid region, the non-rigid region, and the occluded region. In joint unsupervised training of depth and pose, we can segment the occluded region explicitly. The occlusion information is used in unsupervised learning of depth, pose and optical flow, as the image reconstructed by depth, pose and flow will be invalid in occluded regions. A less-than-mean mask is designed to further exclude the mismatched pixels which are interfered with motion or illumination change in the training of depth and pose networks. This method is also used to exclude some trivial mismatched pixels in the training of the flow net. Maximum normalization is proposed for smoothness term of depth-pose networks to restrain degradation in textureless regions. In the occluded region, as depth and camera motion can provide more reliable motion estimation, they can be used to instruct unsupervised learning of flow. Our experiments in KITTI dataset demonstrate that the model based on three regions, full and explicit segmentation of occlusion, rigid region and non-rigid region with corresponding unsupervised losses can improve performance on three tasks significantly.

该研究针对自动驾驶技术中单目序列所含信息研究了单目深度估计、相机自身运动估计和连续帧中的光流估计，并提出了基于三个区域模型、OCCLUSION区域的完整显式分割以及相应的无监督学习方法， 显著提高了三项任务的性能。

无指导深度、光流、姿态及遮挡的三维几何学学习