A Weight-shared Dual-branch Convolutional Neural Network for Unsupervised Dense Depth Prediction and Camera Motion Estimation

Convolutional Neural Network (CNN) can be used to indiscriminately predict dense depth and camera motion from images, however, ignoring the relationship between depth map and camera motion increases the computational burden to label the datasets and limits the accuracy of the results. In this paper, an end-to-end unsupervised dual-branch CNN is proposed to predict a pixel-wise depth map and simultaneously estimate camera pose. In particular, a weight sharing strategy for two branches is designed to increase the connection between depth map and camera motion. Besides, to reduce the impact of photometric noise, the intermediate feature maps are utilized to compute feature errors. Experimental results on the KITTI datasets demonstrate that our method achieves better performance on dense map prediction and camera pose estimation comparing with the state-of-the-art approaches.