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Structure from motion (SfM) is an essential computer vision problem which hasnot been well handled by deep learning. One of the promising trends is to applyexplicit structural constraint, e.g. 3D cost volume, into the network. However,existing methods usually assume accurate camera poses either from GT or othermethods, which is unrealistic in practice. In this work, we design a physicaldriven architecture, namely DeepSFM, inspired by traditional Bundle Adjustment(BA), which consists of two cost volume based architectures for depth and poseestimation respectively, iteratively running to improve both. The explicitconstraints on both depth (structure) and pose (motion), when combined with thelearning components, bring the merit from both traditional BA and emerging deeplearning technology. Extensive experiments on various datasets show that ourmodel achieves the state-of-the-art performance on both depth and poseestimation with superior robustness against less number of inputs and the noisein initialization.

DeepSFM: Structure From Motion Via Deep Bundle Adjustment