Attention-based network for passive non-light-of-sight reconstruction in complex scenes

Passive non-line-of-sight (NLOS) reconstruction has received considerable success in diverse fields. However, the existing reconstruction methods ignore that complex scenes attenuate object-related information and view object-related information and noise in measured images as equivalent, yielding low-quality recovery. We propose an attention-based encoder–decoder (AED) network to tackle this problem. Specifically, we introduce an attention in the attention (A2B) module that can prune the attention layers to help the network focus on the object-related information in the measured images. In addition, we establish several datasets in complex scenes, including varying ambient light conditions and parameter settings of reconstruction systems, as well as complex hidden objects, to verify the generalization of our method. Experiments on our constructed datasets demonstrate that our methods achieve better recovery performance than existing methods, with more robustness to complex scenes.