Stereo Depth Estimation via Self-supervised Contrastive Representation Learning

Abstract

AbstractAccurate stereo depth estimation is crucial for 3D reconstruction in surgery. Self-supervised approaches are more preferable than supervised approaches when limited data is available for training but they can not learn clear discrete data representations. In this work, we propose a two-phase training procedure which entails: (1) Performing Contrastive Representation Learning (CRL) of left and right views to learn discrete stereo features (2) Utilising the trained CRL model to learn disparity via self-supervised training based on the photometric loss. For efficient and scalable CRL training on stereo images we introduce a momentum pseudo-supervised contrastive loss. Qualitative and quantitative performance evaluation on minimally invasive surgery and autonomous driving data shows that our approach achieves higher image reconstruction score and lower depth error when compared to state-of-the-art self-supervised models. This verifies that contrastive learning is effective in optimising stereo-depth estimation with self-supervised models.KeywordsDeep learningDepth3DStereoSelf-supervisedContrastive representation learning