SRA: Fast Removal of General Multipath for ToF Sensors

Freedman, Daniel Z.; Smolin, Yoni; Krupka, Eyal; Leichter, Ido; Schmidt, Matthias

doi:10.1007/978-3-319-10590-1_16

Cited by 95 publications

(90 citation statements)

References 16 publications

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“…We assume that there is no indirect or multi-bounce light component [Freedman et al 2014;Heide et al 2013;Kadambi et al 2013;O'Toole et al 2014], i.e., light from the source bounces only once at a scene point and returns to the sensor. Then, the radiance L(p, t) incident at pixel p at time t is given as:…”

Section: Mathematical Preliminariesmentioning

confidence: 99%

What Are Optimal Coding Functions for Time-of-Flight Imaging?

et al. 2018

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The depth resolution achieved by a continuous wave time-of-ight (C-ToF) imaging system is determined by the coding (modulation and demodulation) functions that it uses. Almost all current C-ToF systems use sinusoid or square coding functions, resulting in a limited depth resolution. In this paper, we present a mathematical framework for exploring and characterizing the space of C-ToF coding functions in a geometrically intuitive space. Using this framework, we design families of novel coding functions that are based on Hamiltonian cycles on hypercube graphs. Given a xed total source power and acquisition time, the new Hamiltonian coding scheme can achieve up to an order of magnitude higher resolution as compared to the current state-of-the art methods, especially in low SNR settings. We also develop a comprehensive physically-motivated simulator for C-ToF cameras that can be used to evaluate various coding schemes prior to a real hardware implementation. Since most o -the-shelf C-ToF sensors use sinusoid or square functions, we develop a hardware prototype that can implement a wide range of coding functions. Using this prototype and our software simulator, we demonstrate the performance advantages of the proposed Hamiltonian coding functions in a wide range of imaging settings.

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Section: Mathematical Preliminariesmentioning

confidence: 99%

What Are Optimal Coding Functions for Time-of-Flight Imaging?

et al. 2018

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“…We use the K-reflector model for describing the impulse response of a pixel with multiple reflectors [18,19]:…”

Section: Imaging Multiple Depths Per Pixelmentioning

confidence: 99%

Computational single-photon depth imaging without transverse regularization

Shin

Shapiro

Goyal

2016

2016 IEEE International Conference on Image Processing (ICIP)

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Depth profile reconstruction of a scene at low light levels using an active imaging setup has wide-ranging applications in remote sensing. In such low-light imaging scenarios, single-photon detectors are employed to time-resolve individual photon detections. However, even with single-photon detectors, current frameworks are limited to using hundreds of photon detections at each pixel to mitigate Poisson noise inherent in light detection. In this paper, we discuss two pixelwise imaging frameworks that allow accurate reconstruction of depth profiles using small numbers of photon detections. The first framework addresses the problem of depth reconstruction of an opaque target, in which it is assumed that each pixel contains exactly one reflector. The second framework addresses the problem of reconstructing multiple-depth pixels. In each scenario, our framework achieves photon efficiency by combining accurate statistics for individual photon detections with a longitudinal sparsity constraint tailored to the imaging problem. We demonstrate the photon efficiencies of our frameworks by comparing them with conventional imagers that use more naïve models based on high light-level assumptions.

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“…5 results in an ill-posed problem [9,10]. This becomes obvious when choosing sinusoidal f and g ,as then C becomes a truncated Fourier matrix, whose maximum temporal frequency is limited by the maximum achievable modulation frequency of typically 25-100 MHz for recent correlation-based camera systems.…”

Section: Transient Imaging In Scattering Mediamentioning

confidence: 99%

“…Several recent works [10,14,15] attempt to resolve multi-path interference by assuming sparsity of i in the Dirac basis. The vastly popular idea of compressed sensing [11] is applied in a straightforward manner, by trying to solve…”

Section: Are Transient Images Sparse?mentioning

confidence: 99%

“…One can see that, although these pixels are dominated by direct reflections, the signals are not sparse at all, and time-domain sparse backscatter models as used by Freedman et al's SRA [10], or Bhandari et al [15] are not capable of close reconstructions. Our method, on the other hand, produces solutions that follow the ground-truth distributions more closely than any of the competing models.…”

Section: Synthetic Evaluation Of Reconstruction Codementioning

confidence: 99%

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