Monocular Depth Estimation using Multi-Scale Continuous CRFs as Sequential Deep Networks
This work addresses depth estimation for computer vision and robotics, offering incremental improvements over existing methods.
The paper tackled monocular depth estimation from a single image by proposing a deep model that fuses multi-scale CNN outputs using continuous Conditional Random Fields (CRFs), implemented as sequential deep networks for end-to-end training, achieving new state-of-the-art results on NYUD-V2, Make3D, and KITTI datasets.
Depth cues have been proved very useful in various computer vision and robotic tasks. This paper addresses the problem of monocular depth estimation from a single still image. Inspired by the effectiveness of recent works on multi-scale convolutional neural networks (CNN), we propose a deep model which fuses complementary information derived from multiple CNN side outputs. Different from previous methods using concatenation or weighted average schemes, the integration is obtained by means of continuous Conditional Random Fields (CRFs). In particular, we propose two different variations, one based on a cascade of multiple CRFs, the other on a unified graphical model. By designing a novel CNN implementation of mean-field updates for continuous CRFs, we show that both proposed models can be regarded as sequential deep networks and that training can be performed end-to-end. Through an extensive experimental evaluation, we demonstrate the effectiveness of the proposed approach and establish new state of the art results for the monocular depth estimation task on three publicly available datasets, i.e. NYUD-V2, Make3D and KITTI.