Passive non-line-of-sight (NLOS) imaging has witnessed rapid development in recent years, due to its ability to image objects that are out of sight. The light transport condition plays an important role in this task since changing the conditions will lead to different imaging models. Existing learning-based NLOS methods usually train independent models for different light transport conditions, which is computationally inefficient and impairs the practicality of the models. In this work, we propose NLOS-LTM, a novel passive NLOS imaging method that effectively handles multiple light transport conditions with a single network. We achieve this by inferring a latent light transport representation from the projection image and using this representation to modulate the network that reconstructs the hidden image from the projection image. We train a light transport encoder together with a vector quantizer to obtain the light transport representation. To further regulate this representation, we jointly learn both the reconstruction network and the reprojection network during training. A set of light transport modulation blocks is used to modulate the two jointly trained networks in a multi-scale way. Extensive experiments on a large-scale passive NLOS dataset demonstrate the superiority of the proposed method. The code is available at https://github.com/JerryOctopus/NLOS-LTM.

翻译：被动非视域（NLOS）成像因能够对视线之外的物体进行成像，近年来取得了快速发展。光传输条件在该任务中扮演着重要角色，因为条件变化会导致不同的成像模型。现有基于学习的NLOS方法通常针对不同光传输条件训练独立模型，这不仅计算效率低下，也损害了模型的实用性。本文提出NLOS-LTM，一种新颖的被动NLOS成像方法，能够通过单一网络有效处理多种光传输条件。我们通过从投影图像中推断潜在的光传输表示，并利用该表示调制从投影图像重建隐藏图像的网络来实现这一目标。我们训练光传输编码器与向量量化器共同获得光传输表示。为进一步规范该表示，我们在训练过程中联合学习重建网络和重投影网络。一组光传输调制块用于以多尺度方式调制这两个联合训练的网络。在大规模被动NLOS数据集上的大量实验证明了所提方法的优越性。代码可在https://github.com/JerryOctopus/NLOS-LTM获取。