A monocular image depth estimation method based on weighted fusion and point-wise convolution

The existing monocular depth estimation methods based on deep learning have difficulty in estimating the depth near the edges of the objects in an image when the depth distance between these objects changes abruptly and decline in accuracy when an image has more noises. Furthermore, these methods consume more hardware resources because they have huge network parameters. To solve these problems, this paper proposes a depth estimation method based on weighted fusion and point-wise convolution. The authors design a maximum-average adaptive pooling weighted fusion module (MAWF) that fuses global features and local features and a continuous point-wise convolution module for processing the fused features derived from the (MAWF) module. The two modules work closely together for three times to perform weighted fusion and point-wise convolution of features of multi-scale from the encoder output, which can better decode the depth information of a scene. Experimental results show that our method achieves state-of-the-art performance on the KITTI dataset with δ₁ up to 0.996 and the root mean square error metric down to 8% and has demonstrated the strong generalisation and robustness.