RESUMEN
Non-line-of-sight (NLOS) imaging aims to reconstruct the three-dimensional hidden scenes by using time-of-flight photon information after multiple diffuse reflections. The under-sampled scanning data can facilitate fast imaging. However, the resulting reconstruction problem becomes a serious ill-posed inverse problem, the solution of which is highly likely to be degraded due to noises and distortions. In this paper, we propose novel NLOS reconstruction models based on curvature regularization, i.e., the object-domain curvature regularization model and the dual (signal and object)-domain curvature regularization model. In what follows, we develop efficient optimization algorithms relying on the alternating direction method of multipliers (ADMM) with the backtracking stepsize rule, for which all solvers can be implemented on GPUs. We evaluate the proposed algorithms on both synthetic and real datasets, which achieve state-of-the-art performance, especially in the compressed sensing setting. Based on GPU computing, our algorithm is the most effective among iterative methods, balancing reconstruction quality and computational time. All our codes and data are available at https://github.com/Duanlab123/CurvNLOS.
RESUMEN
Single-photon light detection and ranging (LiDAR) - offering single-photon sensitivity and picosecond temporal resolution - has become one of the most promising technologies for 3D imaging and target detection. Generally, target detection and identification requires the construction of an image, performed by a raster-scanned or an array-based LiDAR system. In contrast, we demonstrate an image-free target identification approach based on a single-point single-photon LiDAR. The idea is to identify the object from the temporal data equipped with an efficient neural network. Specifically, the target is flood-illuminated by a pulsed laser and a single-point single-photon detector is used to record the time-of-flight (ToF) of back-scattering photons. A deep-learning method is then employed to analyze the ToF data and perform the identification task. Simulations with indoor and outdoor experiments show that our approach can identify the class and pose of the target with high accuracy. Importantly, we construct a compact single-point single-photon LiDAR system and demonstrate the practical capability to identify the types and poses of drones in outdoor environments over hundreds of meters. We believe our approach will be useful in applications for sensing dynamic targets with low-power optical detection.
RESUMEN
Non-line-of-sight (NLOS) imaging has the ability to reconstruct hidden objects from indirect light paths that scatter multiple times in the surrounding environment, which is of considerable interest in a wide range of applications. Whereas conventional imaging involves direct line-of-sight light transport to recover the visible objects, NLOS imaging aims to reconstruct the hidden objects from the indirect light paths that scatter multiple times, typically using the information encoded in the time-of-flight of scattered photons. Despite recent advances, NLOS imaging has remained at short-range realizations, limited by the heavy loss and the spatial mixing due to the multiple diffuse reflections. Here, both experimental and conceptual innovations yield hardware and software solutions to increase the standoff distance of NLOS imaging from meter to kilometer range, which is about three orders of magnitude longer than previous experiments. In hardware, we develop a high-efficiency, low-noise NLOS imaging system at near-infrared wavelength based on a dual-telescope confocal optical design. In software, we adopt a convex optimizer, equipped with a tailored spatial-temporal kernel expressed using three-dimensional matrix, to mitigate the effect of the spatial-temporal broadening over long standoffs. Together, these enable our demonstration of NLOS imaging and real-time tracking of hidden objects over a distance of 1.43 km. The results will open venues for the development of NLOS imaging techniques and relevant applications to real-world conditions.
RESUMEN
Non-line-of-sight (NLOS) imaging techniques have the ability to look around corners, which is of growing interest for diverse applications. We explore compressed sensing in active NLOS imaging and show that compressed sensing can greatly reduce the required number of scanning points without the compromise of the imaging quality. Particularly, we perform the analysis for both confocal NLOS imaging and active occlusion-based periscopy. In experiment, we demonstrate confocal NLOS imaging with only 5 × 5 scanning points for reconstructing a three-dimensional hidden image which has 64 × 64 spatial resolution. The results show that compressed sensing can reduce the scanning points and the total capture time, while keeping the imaging quality. This will be desirable for high speed NLOS applications.
