Monocular Camera Based Real-Time Dense Mapping Using Generative Adversarial Network

Xin Yang, Jinyu Chen, Zhiwei Wang, Qiaozhe Zhang, Wenyu Liu, Chunyuan Liao, Kwang-Ting Cheng

Abstract: Monocular simultaneous localization and mapping (SLAM) is a key enabling technique for many computer vision and robotics applications. However, existing methods either can obtain only sparse or semi-dense maps in highly-textured image areas or fail to achieve a satisfactory reconstruction accuracy. In this paper, we present a new method based on a generative adversarial network,named DM-GAN, for real-time dense mapping based on a monocular camera. Specifcally, our depth generator network takes a semidense map obtained from motion stereo matching as a guidance to supervise dense depth prediction of a single RGB image. The depth generator is trained based on a combination of two loss functions, i.e. an adversarial loss for enforcing the generated depth maps to reside on the manifold of the true depth maps and a pixel-wise mean square error (MSE) for ensuring the correct absolute depth values. Extensive experiments on three public datasets demonstrate that our DM-GAN signifcantly outperforms the state-of-the-art methods in terms of greater reconstruction accuracy and higher depth completeness.