Depth Estimation Algorithm Based on Transformer-Encoder and Feature
  Fusion
Linhan Xia Junbang Liu Tong Wu
This research presents a novel depth estimation algorithm based on a
Transformer-encoder architecture, tailored for the NYU and KITTI Depth Dataset.
This research adopts a transformer model, initially renowned for its success in
natural language processing, to capture intricate spatial relationships in
visual data for depth estimation tasks. A significant innovation of the
research is the integration of a composite loss function that combines
Structural Similarity Index Measure (SSIM) with Mean Squared Error (MSE). This
combined loss function is designed to ensure the structural integrity of the
predicted depth maps relative to the original images (via SSIM) while
minimizing pixel-wise estimation errors (via MSE). This research approach
addresses the challenges of over-smoothing often seen in MSE-based losses and
enhances the model's ability to predict depth maps that are not only accurate
but also maintain structural coherence with the input images. Through rigorous
training and evaluation using the NYU Depth Dataset, the model demonstrates
superior performance, marking a significant advancement in single-image depth
estimation, particularly in complex indoor and traffic environments.