Abstract
Recent works have demonstrated non-line of sight (NLOS) reconstruction by using the time-resolved signal from multiply scattered light. These works combine ultrafast imaging systems with computation, which back-projects the recorded space-time signal to build a probabilistic map of the hidden geometry. Unfortunately, this computation is slow, becoming a bottleneck as the imaging technology improves. In this work, we propose a new back-projection technique for NLOS reconstruction, which is up to a thousand times faster than previous work, with almost no quality loss. We base on the observation that the hidden geometry probability map can be built as the intersection of the three-bounce space-time manifolds defined by the light illuminating the hidden geometry and the visible point receiving the scattered light from such hidden geometry. This allows us to pose the reconstruction of the hidden geometry as the voxelization of these space-time manifolds, which has lower theoretic complexity and is easily implementable in the GPU. We demonstrate the efficiency and quality of our technique compared against previous methods in both captured and synthetic data.
Highlights
One of the core applications of time-resolved imaging is the capability to robustly capture depth from a scene, by being able to track the time of arrival of photons
In this work we propose a new back-projection reconstruction method that yields a speedup factor of three orders of magnitude over previous non-line of sight (NLOS) reconstruction approaches, addressing the main pending issue limiting the applicability of recent approaches
We evaluate our method by using datasets from three different sources, each showing variable ranges of complexity, as well as different levels of signal quality
Summary
One of the core applications of time-resolved imaging (see e.g. [1, 2] for recent surveys on the field) is the capability to robustly capture depth from a scene, by being able to track the time of arrival of photons. Different approaches have been proposed for reconstruction, either by solving a non-convex optimization on three-dimensional geometry [11, 12] or a depth map [8], or by back-projecting the space-time captured image on a voxelized geometry representation [3, 13] In both cases, the large amount of data being processed, together with the complexity of the reconstruction algorithms, impose computation times in the order of several hours. This combination of lower computational complexity and an efficient hardware-accelerated implementation allows us to increase efficiency further: our techniques allows computing the probability maps of large datasets in the order of seconds with a minimum increase in error, while on the other hand allows significantly higher-resolution reconstructions with a negligible added cost We demonstrate these capabilities by evaluating our method using both synthetic and real captured data, and comparing with existing approaches. We make our code publicly available at http://giga.cps.unizar.es/~ajarabo/pubs/ nlosbackprojectionOExp17/code/
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