SwinDepth: Unsupervised Depth Estimation using Monocular Sequences via Swin Transformer and Densely Cascaded Network

Understand

Monocular depth estimation plays a critical role in various computer vision and robotics applications such as localization, mapping, and 3D object detection.

• Recently, learning-based algorithms achieve huge success in depth estimation by training models with a large amount of data in a supervised manner.
• However, it is challenging to acquire dense ground truth depth labels for supervised training, and the unsupervised depth estimation using monocular sequences emerges as a promising alternative.
• Unfortunately, most studies on unsupervised depth estimation explore loss functions or occlusion masks, and there is little change in model architecture in that ConvNet-based encoder-decoder structure becomes a de-facto standard for depth estimation.