2 research outputs found
Self-Supervised Structure-from-Motion through Tightly-Coupled Depth and Egomotion Networks
Much recent literature has formulated structure-from-motion (SfM) as a
self-supervised learning problem where the goal is to jointly learn neural
network models of depth and egomotion through view synthesis. Herein, we
address the open problem of how to optimally couple the depth and egomotion
network components. Toward this end, we introduce several notions of coupling,
categorize existing approaches, and present a novel tightly-coupled approach
that leverages the interdependence of depth and egomotion at training and at
inference time. Our approach uses iterative view synthesis to recursively
update the egomotion network input, permitting contextual information to be
passed between the components without explicit weight sharing. Through
substantial experiments, we demonstrate that our approach promotes consistency
between the depth and egomotion predictions at test time, improves
generalization on new data, and leads to state-of-the-art accuracy on indoor
and outdoor depth and egomotion evaluation benchmarks.Comment: Submitted to NeurIPS 202