Depth Estimation from Monocular Image and Coarse Depth Points based on Conditional GAN

¹ Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
² Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China

^* Corresponding author : qianky@sz.tsinghua.edu.cn

Abstract

Depth estimation has achieved considerable success with the development of the depth sensor devices and deep learning method. However, depth estimation from monocular RGB-based image will increase ambiguity and is prone to error. In this paper, we present a novel approach to produce dense depth map from a single image coupled with coarse point-cloud samples. Our approach learns to fit the distribution of the depth map from source data using conditional adversarial networks and convert the sparse point clouds to dense maps. Our experiments show that the use of the conditional adversarial networks can add full image information to the predicted depth maps and the effectiveness of our approach to predict depth in NYU-Depth-v2 indoor dataset.