DwinFormer: Dual Window Transformers for End-to-End Monocular Depth Estimation

Understand

Depth estimation from a single image is of paramount importance in the realm of computer vision, with a multitude of applications.

• Conventional methods suffer from the trade-off between consistency and fine-grained details due to the local-receptive field limiting their practicality.
• This lack of long-range dependency inherently comes from the convolutional neural network part of the architecture.
• In this paper, a dual window transformer-based network, namely DwinFormer, is proposed, which utilizes both local and global features for end-to-end monocular depth estimation.