Unsupervised domain adaptation algorithm design for robot perception - Tan, Jiawei (Nanyang Technological University, 2021)
Depth completion task is an important direction in the current scene depth estimation field. The overall process of depth completion task is to restore the sparse depth map to a pixel-wide dense depth map with the aid of color image. Because the models that predict depth solely through monocular images is very sensitive to color and texture and has scale uncertainty, depth completion task is the most effective solution for realizing scene depth estimation. In this dissertation, depth completion task as a typical robot perception task is selected. We designed an end-to-end depth completion network: Attention based two-branch fusion network. The model is based on a dual-branch structure. And the two branches respectively focus more on color image input and depth information input for depth prediction. An attention based lightweight fusion module is proposed, which allocates spatial attention to the feature map from the two branches that need to be fused. This fusion module strengthens the transmission of important feature information and reduces the parameter scale of the model. Besides, a branch competition mechanism is designed to make the two branches strengthen their respective branch positions in the confrontation and achieve mutual optimization.