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：无源非视距（NLOS）成像因其能够对视线外物体进行成像的能力，近年来取得了快速发展。光传输条件在该任务中扮演重要角色，因为改变条件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，这既降低了计算效率，也削弱了模型的实用性。本研究提出NLOS-LTM，一种新颖的无源NLOS成像方法，通过单个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。我们联合训练光传输编码器与矢量量化器以获取光传输表示。为进一步规范该表示，我们在训练过程中同时学习重建网络与重投影网络。采用一组光传输调制块以多尺度方式对这两个联合训练的网络进行调制。在大规模无源NLOS数据集上的大量实验证明了该方法的优越性。代码已开源至https://github.com/JerryOctopus/NLOS-LTM。