  The monocular depth estimation task has recently revealed encouraging
prospects, especially for the autonomous driving task. To tackle the ill-posed
problem of 3D geometric reasoning from 2D monocular images, multi-frame
monocular methods are developed to leverage the perspective correlation
information from sequential temporal frames. However, moving objects such as
cars and trains usually violate the static scene assumption, leading to feature
inconsistency deviation and misaligned cost values, which would mislead the
optimization algorithm. In this work, we present CTA-Depth, a Context-aware
Temporal Attention guided network for multi-frame monocular Depth estimation.
Specifically, we first apply a multi-level attention enhancement module to
integrate multi-level image features to obtain an initial depth and pose
estimation. Then the proposed CTA-Refiner is adopted to alternatively optimize
the depth and pose. During the refinement process, context-aware temporal
attention (CTA) is developed to capture the global temporal-context
correlations to maintain the feature consistency and estimation integrity of
moving objects. In particular, we propose a long-range geometry embedding (LGE)
module to produce a long-range temporal geometry prior. Our approach achieves
significant improvements over state-of-the-art approaches on three benchmark
datasets.
