Adaptive Attention Based on Mixture Distribution for Zero-Shot Non-Line-of-Sight Imaging

Non-line-of-sight (NLOS) imaging is an ill-posed problem to reconstruct hidden 3D scenes by leveraging photon time-of-flight information from diffusely reflected light. In the existing regularization models, the spatial residuals were handled by a single distribution, failing to account for the distinct characteristics of background and target objects. In this paper, we propose a novel NLOS reconstruction method that models the non-Gaussian residuals with a mixture distribution. Through a dual method, we derive an adaptive weighted residual model, where the weights generated in the dual space act as a zero-shot attention mechanism to control the contributions of different regions. The corresponding optimization problem can be effectively solved using the alternating minimization algorithm. Numerical experiments on both synthetic and real-world datasets demonstrate that our method surpasses the related existing approaches, achieving state-of-the-art performance. The code is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/qiuxuanzhizi/AMD-NLOS-V1</uri>.