APAC-Net: Unsupervised Learning of Depth and Ego-Motion from Monocular Video

We propose an unsupervised novel method, Attention-Pixel and Attention-Channel Network (APAC-Net), for unsupervised monocular learning of estimating scene depth and ego-motion. Our model only utilizes monocular image sequences and does not need additional sensor information, such as IMU and GPS, for supervising. The attention mechanism is employed in APAC-Net to improve the networks' efficiency. Specifically, three attention modules are proposed to adjust feature weights when training. Moreover, to minimum the effect of noise, which is produced in the reconstruction processing, the Image-reconstruction loss based on PSNR LPSNR is used to evaluation the reconstruction quality. In addition, due to the fail depth estimation of the objects closed to camera, the Temporal-consistency loss LTemp between adjacent frames and the Scale-based loss LScale among different scales are proposed. Experimental results showed APAC-Net can perform well in both the depth and ego-motion tasks, and it even behaved better in several items on KITTI and Cityscapes.