Self-Supervised Monocular Depth Estimation With Frequency-Based Recurrent Refinement

Abstract

Self-supervised monocular depth estimation has succeeded in learning scene geometry from only image pairs or sequences. However, it is still highly ill-posed for self-supervised depth estimation to generate high-quality depth maps with both global high accuracy and local fine details. To address this issue, we propose a novel frequency-based recurrent refinement scheme to improve the self-supervised depth estimation. Since the global and local depth representation can be correlated to high/low frequency coefficients in the frequency domain, we propose a frequency-based recurrent depth coefficient refinement (RDCR) scheme, which progressively refines both low frequency and high frequency depth coefficients with an RNN-based architecture in a multi-level manner. During the recurrent process, the depth coefficients generated from the previous time step are used as the input to generate the current depth coefficients, yielding progressively optimized depth estimations. Meanwhile, considering that the depth details often appear in areas with high image frequency, we further improve depth details during the RDCR process by leveraging the image-based high frequency components. Specifically, in each RDCR module, we enhance the high frequency depth representations by selecting and feeding the informative image-based high frequency features with a learned feature weighting mask. Extensive experiments show that the proposed method achieves globally accurate estimation with fine local details, outperforming other self-supervised methods in both quantitative and qualitative comparisons.