3D measurement by estimating Homogeneous Light Transport (HLT) Matrix

Chiba, Nobuo; Hashimoto, Koichi

doi:10.1109/icma.2017.8016084

Cited by 11 publications

(7 citation statements)

References 16 publications

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“…13. It is well known that the accuracy of fringe profilometry is susceptible to global illumination [6], [23], while the global illumination effects might be reduced in the estimated LTM. In our experiments, a point is considered an outlier if the distance between two corresponding points in the real and virtual 3D reconstruction is larger than 5 mm.…”

Section: Resultsmentioning

confidence: 99%

“…Remove columns from the measurement matrix that include values of zero at the corresponding indices of the index map {I P i } and obtain the reduced measurement matrix R . 4 Perform sparse optimization as in (6) in the reduced search space and obtain the estimation of the reduced light transport matrix t R i . 5 Reorder the coefficients of the reduced light transport t R i into the high-resolution LTM using the previous mapping and obtain the high-resolution light transport estimate t i .…”

Section: Dual Imaging and Virtual Relightingmentioning

confidence: 99%

“…To create a fair comparison framework, we use the SPAMS optimization toolbox [22] for solving the sparse optimization problem and use the unconstrained form as in (6). Parameter λ is set to 1e−3.…”

Section: B Experimentsmentioning

confidence: 99%

“…All computation is performed in sparse arithmetic to reduce the memory footprint. In the final step of rapid LTM estimation using the proposed procedure, we again use the SPAMS optimization toolbox to solve the sparse optimization problem defined in (6).…”

Section: B Experimentsmentioning

confidence: 99%

“…In [6], the authors introduce a homogeneous light transport matrix, in which the LTM and background projector light are described by a single matrix and estimated using CS principles. That approach enables efficient removal of the projector background light from the LTM estimate.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Dual Imaging–Can Virtual Be Better Than Real?

2020

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In this paper, a dual imaging technique is used for high-precision reconstruction of an observed 3D scene. In contrast to stereo vision, dual imaging systems use a camera and a projector instead of a camera pair. We propose a multiresolution approach based on the sum-to-one transform, coupled with compressive sensing principles, for efficient estimation of the light transport matrix (LTM). The LTM contains information on both optical systems and the 3D scene. In our setup, the camera sensor is intentionally chosen to be low resolution to prove the future use of inexpensive sensors in nonvisible regions of the light spectrum, as well as the potential design of simplified multiview and light field acquisition systems. We show that a highprecision estimation of the LTM from a reduced set of measurements is possible. Virtual measurements, instead of physical, are conducted to obtain the 3D reconstruction. We show that 3D scene reconstruction from the proposed virtual measurements corresponds with the actual physical acquisition. Moreover, this approach provides much more detail in the reconstruction. The computational complexity of the proposed methods is reduced to such a level that practical implementations are feasible.

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Section: Resultsmentioning

confidence: 99%

Section: Dual Imaging and Virtual Relightingmentioning

confidence: 99%

Section: B Experimentsmentioning

confidence: 99%

Section: B Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dual Imaging–Can Virtual Be Better Than Real?

2020

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Projective Parallel Single-Pixel Imaging to Overcome Global Illumination in 3D Structure Light Scanning

Zhao

Jiang

et al. 2022

Lecture Notes in Computer Science

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We present projective parallel single-pixel imaging (pPSI), a 3D photography method that provides a robust and efficient way to analyze the light transport behavior and enables separation of light effect due to global illumination, thereby achieving 3D structured light scanning under global illumination. The light transport behavior is described by the light transport coefficients (LTC), which contain complete information for a projector-camera pair, and is a 4D data set. However, the capture of LTC is generally time consuming. The 4D LTC in pPSI are reduced to projection functions, thereby enabling a highly efficient data capture process. We introduce the local maximum constraint, which provides constraint for the location of candidate correspondence matching points when projections are captured. Local slice extension (LSE) method is introduced to accelerate the capture of projection functions. Optimization is conducted for pPSI under several situations. The number of projection functions required for pPSI is optimized and the influence of capture ratio in LSE on the accuracy of the correspondence matching points is investigated. Discussions and experiments include two typical kinds of global illuminations: inter-reflections and subsurface scattering. The proposed method is validated with several challenging scenarios, and outperforms the state-ofthe-art methods.

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Blind inverse light transport using unrolling network

Yin,

Li,

et al. 2023

Appl Intell

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3D measurement by estimating Homogeneous Light Transport (HLT) Matrix

Cited by 11 publications

References 16 publications

Dual Imaging–Can Virtual Be Better Than Real?

Dual Imaging–Can Virtual Be Better Than Real?

Projective Parallel Single-Pixel Imaging to Overcome Global Illumination in 3D Structure Light Scanning

Blind inverse light transport using unrolling network

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