Estimating Depth from Monocular Images as Classification Using Deep Fully Convolutional Residual Networks

Abstract

Depth estimation from single monocular images is a key component of scene understanding and has benefited largely from deep convolutional neural networks (CNN) recently. In this article, we take advantage of the recent deep residual networks and propose a simple yet effective approach to this problem. We formulate depth estimation as a pixel-wise classification task. Specifically, we first discretize the continuous depth values into multiple bins and label the bins according to their depth range. Then we train fully convolutional deep residual networks to predict the depth label of each pixel. Performing discrete depth label classification instead of continuous depth value regression allows us to predict a confidence in the form of probability distribution. We further apply fully-connected conditional random fields (CRF) as a post processing step to enforce local smoothness interactions, which improves the results. We evaluate our approach on both indoor and outdoor datasets and achieve state-of-the-art performance.

Accepted to IEEE Transactions on Circuits and Systems for Video Technology

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Cited by in corpus (6)

• Deep Learning-based Depth Estimation Methods from Monocular Image and Videos: A Comprehensive Survey
• Unsupervised Monocular Depth Estimation with Left-Right Consistency
• Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation
• Parse Geometry from a Line: Monocular Depth Estimation with Partial Laser Observation
• Unsupervised Video Depth Estimation Based on Ego-motion and Disparity Consensus
• Synthesizing Photorealistic Images with Deep Generative Learning