DRO: Deep Recurrent Optimizer for Structure-from-Motion
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There are increasing interests of studying the structure-from-motion (SfM) problem with machine learning techniques. While earlier methods directly learn a mapping from images to depth maps and camera poses, more recent works enforce multi-view geometry through optimization embed in the learning framework. This paper presents a novel optimization method based on recurrent neural networks to further exploit the potential of neural networks in SfM. Our neural optimizer alternatively updates the depth and camera poses through iterations to minimize a feature-metric cost. Two gated recurrent units are designed to trace the historical information during the iterations. Our network works as a zeroth-order optimizer, where the computation and memory expensive cost volume or gradients are avoided. Experiments demonstrate that our recurrent optimizer effectively reduces the feature-metric cost while refining the depth and poses. Our method outperforms previous methods and is more efficient in computation and memory consumption than cost-volume-based methods. The code of our method will be made public.
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