14 research outputs found
Steady-state Non-Line-of-Sight Imaging
Conventional intensity cameras recover objects in the direct line-of-sight of
the camera, whereas occluded scene parts are considered lost in this process.
Non-line-of-sight imaging (NLOS) aims at recovering these occluded objects by
analyzing their indirect reflections on visible scene surfaces. Existing NLOS
methods temporally probe the indirect light transport to unmix light paths
based on their travel time, which mandates specialized instrumentation that
suffers from low photon efficiency, high cost, and mechanical scanning. We
depart from temporal probing and demonstrate steady-state NLOS imaging using
conventional intensity sensors and continuous illumination. Instead of assuming
perfectly isotropic scattering, the proposed method exploits directionality in
the hidden surface reflectance, resulting in (small) spatial variation of their
indirect reflections for varying illumination. To tackle the shape-dependence
of these variations, we propose a trainable architecture which learns to map
diffuse indirect reflections to scene reflectance using only synthetic training
data. Relying on consumer color image sensors, with high fill factor, high
quantum efficiency and low read-out noise, we demonstrate high-fidelity color
NLOS imaging for scene configurations tackled before with picosecond time
resolution
Non-Line-of-Sight Passive Acoustic Localization Around Corners
Non-line-of-sight (NLoS) imaging is an important challenge in many fields
ranging from autonomous vehicles and smart cities to defense applications.
Several recent works in optics and acoustics tackle the challenge of imaging
targets hidden from view (e.g. placed around a corner) by measuring
time-of-flight (ToF) information using active SONAR/LiDAR techniques,
effectively mapping the Green functions (impulse responses) from several
sources to an array of detectors. Here, leveraging passive correlations-based
imaging techniques, we study the possibility of acoustic NLoS target
localization around a corner without the use of controlled active sources. We
demonstrate localization and tracking of a human subject hidden around the
corner in a reverberating room, using Green functions retrieved from
correlations of broadband noise in multiple detectors. Our results demonstrate
that the controlled active sources can be replaced by passive detectors as long
as a sufficiently broadband noise is present in the scene.Comment: 6 pages, 3 figure
Few-shot Non-line-of-sight Imaging with Signal-surface Collaborative Regularization
The non-line-of-sight imaging technique aims to reconstruct targets from
multiply reflected light. For most existing methods, dense points on the relay
surface are raster scanned to obtain high-quality reconstructions, which
requires a long acquisition time. In this work, we propose a signal-surface
collaborative regularization (SSCR) framework that provides noise-robust
reconstructions with a minimal number of measurements. Using Bayesian
inference, we design joint regularizations of the estimated signal, the 3D
voxel-based representation of the objects, and the 2D surface-based description
of the targets. To our best knowledge, this is the first work that combines
regularizations in mixed dimensions for hidden targets. Experiments on
synthetic and experimental datasets illustrated the efficiency and robustness
of the proposed method under both confocal and non-confocal settings. We report
the reconstruction of the hidden targets with complex geometric structures with
only confocal measurements from public datasets, indicating an
acceleration of the conventional measurement process by a factor of 10000.
Besides, the proposed method enjoys low time and memory complexities with
sparse measurements. Our approach has great potential in real-time
non-line-of-sight imaging applications such as rescue operations and autonomous
driving.Comment: main article: 10 pages, 7 figures supplement: 11 pages, 24 figure
A Calibration Scheme for Non-Line-of-Sight Imaging Setups
The recent years have given rise to a large number of techniques for "looking
around corners", i.e., for reconstructing occluded objects from time-resolved
measurements of indirect light reflections off a wall. While the direct view of
cameras is routinely calibrated in computer vision applications, the
calibration of non-line-of-sight setups has so far relied on manual measurement
of the most important dimensions (device positions, wall position and
orientation, etc.). In this paper, we propose a semi-automatic method for
calibrating such systems that relies on mirrors as known targets. A roughly
determined initialization is refined in order to optimize a spatio-temporal
consistency. Our system is general enough to be applicable to a variety of
sensing scenarios ranging from single sources/detectors via scanning
arrangements to large-scale arrays. It is robust towards bad initialization and
the achieved accuracy is proportional to the depth resolution of the camera
system. We demonstrate this capability with a real-world setup and despite a
large number of dead pixels and very low temporal resolution achieve a result
that outperforms a manual calibration
Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar
Conventional sensor systems record information about directly visible
objects, whereas occluded scene components are considered lost in the
measurement process. Non-line-of-sight (NLOS) methods try to recover such
hidden objects from their indirect reflections - faint signal components,
traditionally treated as measurement noise. Existing NLOS approaches struggle
to record these low-signal components outside the lab, and do not scale to
large-scale outdoor scenes and high-speed motion, typical in automotive
scenarios. In particular, optical NLOS capture is fundamentally limited by the
quartic intensity falloff of diffuse indirect reflections. In this work, we
depart from visible-wavelength approaches and demonstrate detection,
classification, and tracking of hidden objects in large-scale dynamic
environments using Doppler radars that can be manufactured at low-cost in
series production. To untangle noisy indirect and direct reflections, we learn
from temporal sequences of Doppler velocity and position measurements, which we
fuse in a joint NLOS detection and tracking network over time. We validate the
approach on in-the-wild automotive scenes, including sequences of parked cars
or house facades as relay surfaces, and demonstrate low-cost, real-time NLOS in
dynamic automotive environments.Comment: First three authors contributed equally; Accepted at CVPR 202