Single-Stage Refinement CNN for Depth Estimation in Monocular Images

José E. Valdez Rodríguez, Hiram Calvo, Edgardo M. Felipe Riverón


Depth reconstruction from single monocular images has been a challenging task due to the complexity and the quantity of depth cues that images have. Convolutional Neural Networks (CNN) have been successfully used to reconstruct depth of general object scenes; however, proposed works use several stages of training which make this process more complex and time consuming. As we aim to build a computational efficient model, we focus on single-stage training CNN. In this paper, we propose five different models for solving this task, ranging from a simple convolutional network, to one with residual, convolutional, refinement and upsampling layers. We compare our models with the current state of the art in depth reconstruction and measure depth reconstruction error for different datasets (KITTI, NYU), obtaining improvements in both global and local error measures.


Depth reconstruction, convolutional neural networks, single stage training, embedded refinement layer, stereo matching

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