Research on Self-Supervised Depth Estimation for Panoramic Images
Depth estimation plays an important role in the fields of virtual reality,scene reconstruction,autonomous driving and object detection.Panoramic image contains omnidirectional visual field information,which has gradually become a research hotspot in depth estimation field.However,the panoramic image has the problem of image distortion,and it is difficult to collect and label the depth data.To this end,by using self-supervised deep learning algorithm,channel optimization multi-space fusion attention mechanism is introduced,and remote feature extraction is enhanced to obtain global and local information.At the same time,the panoramic receptive field is introduced to expand the receptive field to obtain multi-scale information.
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