Weakly Supervised Deep Depth Prediction Leveraging Ground Control Points for Guidance

Despite the tremendous progress made in learning-based depth prediction, most methods rely heavily on large amounts of dense ground-truth depth data for training. To solve the tradeoff between the labeling cost and precision, we propose a novel weakly supervised approach, namely, the Guided-Net, by incorporating robust ground control points for guidance. By exploiting the guidance from ground control points, disparity edge gradients, and image appearance constraints, our improved network with deformable convolutional layers is empowered to learn in a more efficient way. The experiments on the KITTI, Cityscapes, and Make3D datasets demonstrate that the proposed method yields a performance superior to that of the existing weakly supervised approaches and achieves results comparable to those of the semisupervised and supervised frameworks.