Signal capture is at the forefront of perceiving and understanding the environment; thus,
imaging plays a pivotal role in mobile vision. Recent unprecedented progress in artificial …

Abstract Electromagnetic Inverse Scattering Problems (EISP) have gained wide applications
in computational imaging. By solving EISP, the internal relative permittivity of the scatterer …

Passive non-line-of-sight (NLOS) imaging has potential applications in autonomous driving
and search and rescue, but current deep learning approaches often produce suboptimal …

Non-line-of-sight imaging recovers hidden objects around the corner by analyzing the
diffuse reflection light on the relay surface that carries hidden scene information. Due to its …

Abstract Non-line-of-sight (NLOS) tracking has drawn increasing attention in recent years,
due to its ability to detect object motion out of sight. Most previous works on NLOS tracking …

Non-line-of-sight imaging (NLOS) is the process of estimating information about a scene that
is hidden from the direct line of sight of the camera. NLOS imaging typically requires time …

In non-line-of-sight (NLOS) imaging, the spatial information of hidden targets is
reconstructed from the time-of-light (TOF) of the multiple bounced signal photons. The need …

Non-line-of-sight (NLOS) technology has been rapidly developed in recent years, allowing
us to visualize or localize hidden objects by analyzing the returned photons, which is …

Recovering information from non-line-of-sight (NLOS) imaging is a computationally-
intensive inverse problem. Most physics-based NLOS imaging methods address the …

Nearly all existing human pose estimation techniques address the problem under the line-of-
sight (LOS) setting. Many real-life applications such as rescue missions and autonomous …