Monocular Depth Estimation via Deep Structured Models with Ordinal Constraints

Daniel Ron, Kun Duan, Chongyang Ma, Ning Xu, Shenlong Wang, Sumant Hanumante, Dhritiman Sagar

Abstract: User interaction provides useful information for solving challenging computer vision problems in practice. In this paper, we show that a very limited number of user clicks could greatly boost monocular depth estimation performance and overcome monocular ambiguities. We formulate this task as a deep structured model, in which the structured pixel-wise depth estimation has ordinal constraints introduced by user clicks. We show that the inference of the proposed model could be efficiently solved through a feed-forward network. We demonstrate the effectiveness of the proposed model on NYU Depth V2 and Stanford 2D-3D datasets. On both datasets, we achieve state-of-the-art performance when encoding user interaction into our deep models.