Monocular Dense Reconstruction by Depth Estimation Fusion

Dense and accurate reconstruction plays a fundamental role in mobile robot's environment perception and navigation. It's also necessary for obstacle avoidance and path planning of mobile robots. We propose a method to incrementally reconstruct the scene from monocular sequence by fusing the depth from geometry computation and generative adversarial networks (GAN) prediction. The depth from geometry triangulation is precise but sparse, while the depth from GAN is dense but unsealed. In this paper, we combine the advantages from two methods with a linear model optimized by graph structure. Experiments showed that our proposed method gives precise dense reconstruction in real time,