Attention meets Geometry: Geometry Guided Spatial-Temporal Attention for Consistent Self-Supervised Monocular Depth Estimation

• URL: http://arxiv.org/abs/2110.08192v1
• Date: Fri, 15 Oct 2021 16:43:31 GMT
• Title: Attention meets Geometry: Geometry Guided Spatial-Temporal Attention for Consistent Self-Supervised Monocular Depth Estimation
• Authors: Patrick Ruhkamp, Daoyi Gao, Hanzhi Chen, Nassir Navab, Benjamin Busam
• Abstract summary: This paper explores how the increasingly popular transformer architecture, together with novel regularized loss formulations, can improve depth consistency. We propose a spatial attention module that correlates coarse depth predictions to aggregate local geometric information. A novel temporal attention mechanism further processes the local geometric information in a global context across consecutive images.
• Score: 42.249533907879126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inferring geometrically consistent dense 3D scenes across a tuple of temporally consecutive images remains challenging for self-supervised monocular depth prediction pipelines. This paper explores how the increasingly popular transformer architecture, together with novel regularized loss formulations, can improve depth consistency while preserving accuracy. We propose a spatial attention module that correlates coarse depth predictions to aggregate local geometric information. A novel temporal attention mechanism further processes the local geometric information in a global context across consecutive images. Additionally, we introduce geometric constraints between frames regularized by photometric cycle consistency. By combining our proposed regularization and the novel spatial-temporal-attention module we fully leverage both the geometric and appearance-based consistency across monocular frames. This yields geometrically meaningful attention and improves temporal depth stability and accuracy compared to previous methods.

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