Unsupervised monocular depth estimation based on edge enhancement
To solve the problem of poor edge depth estimation accuracy in unsupervised monocular depth estimation,an unsupervised monocular depth estimation model based on edge enhancement is proposed.The model is composed of a single-view depth network and a camera pose estimation network,both of which adopt encoder-decoder structures.The single-view depth network encoder uses high-resolution net(HRNet)as the backbone which maintains high resolution representations throughout the whole process,and is conducive to extract accurate spatial features;The single-view depth network decoder introduces strip convolutions to refine the depth variations near the edges,while enhancing the edge details using the classical Laplace of Gaussian operator.The method fully utilizes the depth edge information to improve the quality of the depth estimation.The experimental results on the KITTI dataset show that the proposed model has good depth estimation performance,making the edges of the depth map clearer with more details.
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