Abstract
To estimate the accurate depth from a single image, we proposed a novel and effective depth estimation architecture to solve the problem of missing and blurred contours of small objects in the depth map. The architecture consists of Extremely Effective Spatial Pyramid modules (EESP) and Pixel Shuffle upsampling Decoders (PSD). The results of this study show that multilevel information and the upsampling method in the decoders are essential for recovering the accurate depth map. Through the model we proposed, competitive performance compared with state-of-the-art methods in terms of reconstruction of object boundaries and the detection rate of small objects has been demonstrated. Our approach has wide applications in higher-level visual tasks, including 3D reconstruction and autonomous driving.
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