Precision depth estimation plays a key role in many applications, including 3D scene reconstruction, virtual reality, autonomous driving and human-computer interaction. Recent advancements in deep learning technologies, the monocular depth estimation has surpassed the traditional stereo camera systems, bringing new possibilities in 3D sensing. In this paper, by using single camera, we propose an end-to-end supervised monocular depth estimation autoencoder which contains a CNN-ViT encoder and an adaptive fusion decoder, to obtain high-precision depth maps. In the CNN-ViT encoder, we construct a multi-scale feature extractor by mixing residual configurations of vision transformers to enhance both local and global information. In the adaptive fusion decoder, we introduce adaptive fusion modules to effectively merge features of the encoder and decoder together. Lastly, the model is trained using a loss function that aligns with human perception to enable it to focus on the depth values of foreground objects. Experimental results demonstrate the effective prediction of the depth map from a single-view color image by the proposed RVTAF autoencoder.