ARO-DeepSFM: deep structure-from-motion with alternating recursive optimization

Abstract

Structure from Motion (SfM) is the cornerstone of 3D reconstruction and visualization of SLAM. Existing deep learning approaches formulate problems by restoring absolute pose ratios from two consecutive frames or predicting a depth map from a single image, both of which are unsuitable problems. In order to solve this maladaptation problem and further tap the potential of neural networks in SfM, this paper proposes a new optimization model for deep motion structure recovery based on recurrent neural networks. The model consists of two architectures based on depth and posture estimation of costs, and is constantly iteratively updated alternately to improve both systems. The neural optimizer designed here tracks historical information during iterations to minimize feature metric cost update depth and camera poses. Experiments show that the optimization model of deep motion structure recovery in this paper is superior to the previous method, effectively reducing the cost of feature-metric, while refining depth and poses.