Monocular Segment-Wise Depth: Monocular Depth Estimation Based on a Semantic Segmentation Prior

Monocular depth estimation using novel learning-based approaches has recently emerged as a promising potential alternative to more conventional 3D scene capture technologies within real-world scenarios. Many such solutions often depend on large quantities of ground truth depth data, which is rare and often intractable to obtain. Others attempt to estimate disparity as an intermediary step using a secondary supervisory signal, leading to blurring and other undesirable artefacts. In this paper, we propose a monocular depth estimation approach, which employs a jointly-trained pixel-wise semantic understanding step to estimate depth for individually-selected groups of objects (segments) within the scene. The separate depth outputs are efficiently fused to generate the final result. This creates more simplistic learning objectives for the jointly-trained individual networks, leading to more accurate overall depth. Extensive experimentation demonstrates the efficacy of the proposed approach compared to contemporary state-of-the-art techniques within the literature.