Passive Non-Line-of-Sight Imaging Based on Diffuse Reflection

Non-Line-of-Sight (NLOS) imaging, which combines imaging and computational reconstruction, describes the reconstruction of hidden scenes in a medium by capturing scattered or reflected information without directly imaging the scene. NLOS imaging is still in the early stages of its development, and systematic research methods for scene modeling and target information reconstruction are lacking. To address these issues, an NLOS imaging method for unobstructed and non-self-luminous scenes is proposed. Based on optical radiation theory, the relationship between the imaging of diffuse reflection surfaces in the scene and the shape of hidden objects is analyzed to determine the NLOS imaging model and reconstruction targets. A Diffuse reflection full-Shadow passive NLOS (DS-NLOS) dataset that resembles physical reality is generated by combining a rendering software with the Motion Picture Experts Group 7 (MPEG7) dataset . A passive NLOS Reconstruction network model (Re-NLOS) is constructed using a Visual Transformer (ViT) structure in combination with a Generative Adversarial Network (GAN) to extract global features from captured diffuse reflection surface images and recover the shape of hidden objects. Experimental results on the DS-NLOS dataset demonstrate that this method can recover the shape information of hidden objects from diffusely reflected surfaces. In comparison with the diffuse reflection full-shadow images, the average Peak Signal-to-Noise Ratio (PSNR) for 20 object categories in the present test set is increased by 5.85 dB, and the average Structural SIMilarity (SSIM ) is increased by 0.038 1. This method also demonstrates restore capabilities in real indoor scenes.