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Semi-Supervised Disparity Estimation with Deep Feature Reconstruction

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arxiv 2106.00318 v1 pith:OXIQ2DDD submitted 2021-06-01 cs.CV

Julia Guerrero-Viu , Sergio Izquierdo , Philipp Schröppel , Thomas Brox This is my paper · ORCID
classification cs.CV
keywords deepdisparityestimationdatadomainfeaturereconstructionsemi-supervised
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Despite the success of deep learning in disparity estimation, the domain generalization gap remains an issue. We propose a semi-supervised pipeline that successfully adapts DispNet to a real-world domain by joint supervised training on labeled synthetic data and self-supervised training on unlabeled real data. Furthermore, accounting for the limitations of the widely-used photometric loss, we analyze the impact of deep feature reconstruction as a promising supervisory signal for disparity estimation.

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