This paper addresses the problem of estimating the depth map of a scene given a single RGB image. We propose a fully convolutional architecture, encompassing residual learning, to model the ambiguous mapping between monocular images and depth maps. In order to improve the output resolution, we present a novel way to efficiently learn feature map up-sampling within the network. For optimization, we introduce the reverse Huber loss that is particularly suited for the task at hand and driven by the value distributions commonly present in depth maps. Our model is composed of a single architecture that is trained end-toend and does not rely on post-processing techniques, such as CRFs or other additional refinement steps. As a result, it runs in real-time on images or videos. In the evaluation, we show that the proposed model contains fewer parameters and requires fewer training data than the current state of the art, while outperforming all approaches on depth estimation. Code and models are publicly available 5 .
Laina et al. REF trained an end-to-end fully convolutional network with residual connections and introduced the reversed Huber loss for this task.
Deeper Depth Prediction with Fully Convolutional Residual Networks
2016 Fourth International Conference on 3D Vision (3DV)
2016 Fourth International Conference on 3D Vision (3DV)
Computer Science