AcED: Accurate and Edge-consistent Monocular Depth Estimation

• URL: http://arxiv.org/abs/2006.09243v1
• Date: Tue, 16 Jun 2020 15:21:00 GMT
• Title: AcED: Accurate and Edge-consistent Monocular Depth Estimation
• Authors: Kunal Swami, Prasanna Vishnu Bondada, Pankaj Kumar Bajpai
• Abstract summary: Single image depth estimation is a challenging problem. We formulate a fully differentiable ordinal regression and train the network in end-to-end fashion. A novel per-pixel confidence map computation for depth refinement is also proposed.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Single image depth estimation is a challenging problem. The current state-of-the-art method formulates the problem as that of ordinal regression. However, the formulation is not fully differentiable and depth maps are not generated in an end-to-end fashion. The method uses a na\"ive threshold strategy to determine per-pixel depth labels, which results in significant discretization errors. For the first time, we formulate a fully differentiable ordinal regression and train the network in end-to-end fashion. This enables us to include boundary and smoothness constraints in the optimization function, leading to smooth and edge-consistent depth maps. A novel per-pixel confidence map computation for depth refinement is also proposed. Extensive evaluation of the proposed model on challenging benchmarks reveals its superiority over recent state-of-the-art methods, both quantitatively and qualitatively. Additionally, we demonstrate practical utility of the proposed method for single camera bokeh solution using in-house dataset of challenging real-life images.

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