Self-Supervised Monocular Depth Estimation With Dual-Path Encoders and Offset Field Interpolation

Abstract

Although self-supervised learning approaches have demonstrated tremendous potential in multi-frame depth estimation scenarios, existing methods struggle to perform well in cases involving dynamic targets and static ego-camera conditions. To address this issue, we propose a self-supervised monocular depth estimation method featuring dual-path encoders and learnable offset interpolation (LOI). First, we construct a dual-path encoding scheme that utilizes residual and transformer blocks to extract both single- and multi-frame features from the input frames. We design a contrastive learning strategy to effectively decouple single- and multi-frame features, enabling weighted fusion guided by a confidence map. Next, we explore two distinct decoding heads for simultaneously generating low-resolution predictions and offset fields. We then design an LOI module to directly upsample a low-resolution depth map to a full-resolution map. This one-step decoding framework enables accurate and efficient depth prediction. Finally, we evaluate our proposed method on the KITTI and Cityscapes benchmarks, conducting a comprehensive comparison with state-of-the-art approaches. The experimental results demonstrate that our DualDepth method achieves competitive performance in terms of both estimation accuracy and efficiency.