Reconstructing Gas Flows Using Light-Path Approximation

Ji, Yu; Ye, Jinwei; Yu, Jingyi

doi:10.1109/cvpr.2013.324

Cited by 49 publications

(16 citation statements)

References 26 publications

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“…By using this technique, the transparent surface can be reconstructed [22]. Similarly, Ji et al [23] also utilized the LF-probe and multiple viewpoints to reconstruct the invisible gas flow. Although this technique can reconstruct the transparent surface and invisible gas flow, it also has restricted practical use as the LF-probe is always required as a background object.…”

Section: Related Workmentioning

confidence: 97%

Light field distortion feature for transparent object classification

Maeno

Nagahara

et al. 2015

Computer Vision and Image Understanding

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Section: Related Workmentioning

confidence: 97%

Light field distortion feature for transparent object classification

Maeno

Nagahara

et al. 2015

Computer Vision and Image Understanding

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“…Recall that we have ignored the effects of occlusion and specular reflection. The curious reader is referred to Durand et al [2005] for a discussion of specularly reflective surfaces and occlusions in the context of the light field, Maeno et al [2013] for scenes with refractive objects, Ji et al [2013] for an excellent treatment of the more complex case of refractive gas flows, and Raskar et al [2008] for situations where the camera itself contributes complex lens flare effects.…”

Section: Parallax In 2dmentioning

confidence: 99%

Linear Volumetric Focus for Light Field Cameras

2015

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We demonstrate that the redundant information in light field imagery allows volumetric focus, an improvement of signal quality that maintains focus over a controllable range of depths. To do this, we derive the frequencydomain region of support of the light field, finding it to be the 4D hyperfan at the intersection of a dual fan and a hypercone, and design a filter with correspondingly shaped passband. Drawing examples from the Stanford Light Field Archive and images captured using a commercially available lenslet-based plenoptic camera, we demonstrate that the hyperfan outperforms competing methods including planar focus, fan-shaped antialiasing, and nonlinear image and video denoising techniques. We show the hyperfan preserves depth of field, making it a single-step all-in-focus denoising filter suitable for general-purpose light field rendering. We include results for different noise types and levels, through murky water and particulate matter, in real-world scenarios, and evaluated using a variety of metrics. We show that the hyperfan's performance scales with aperture count, and demonstrate the inclusion of aliased components for high-quality rendering.

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“…Specifically, reconstructing transparent objects is well-known a challenging problem [Ihrke et al 2010]. Recent developments, such as reconstruction of flames [Ihrke and Magnor 2004;Wu et al 2015b], mixing fluids [Gregson et al 2012], gas flow [Atcheson et al 2008;Ji et al 2013], and cloud [Levis et al 2015[Levis et al , 2017, aim at dynamic inhomogeneous transparent objects, whereas we focus on static reflective and refractive surfaces Fig. 2.…”

Section: Related Workmentioning

confidence: 99%

“…Tsai et al [2015] consider two-refraction cases instead. Note that with given incident and exit ray-ray correspondences [Iseringhausen et al 2017;Ji et al 2013;Wetzstein et al 2011], depth-normal ambiguity still exists as they are interrelated with each other. Qian et al [2016] propose a position-normal consistency constraint for solving the two-refraction reconstruction problem, but they only compute a pair of front-back surface depth maps.…”

Section: Direct Ray Measurements Kutulakos and Steger [Kutulakos Andmentioning

confidence: 99%

Full 3D reconstruction of transparent objects

Zhou

Qian

et al. 2018

ACM Trans. Graph.

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Numerous techniques have been proposed for reconstructing 3D models for opaque objects in past decades. However, none of them can be directly applied to transparent objects. This paper presents a fully automatic approach for reconstructing complete 3D shapes of transparent objects. Through positioning an object on a turntable, its silhouettes and light refraction paths under different viewing directions are captured. Then, starting from an initial rough model generated from space carving, our algorithm progressively optimizes the model under three constraints: surface and refraction normal consistency, surface projection and silhouette consistency, and surface smoothness. Experimental results on both synthetic and real objects demonstrate that our method can successfully recover the complex shapes of transparent objects and faithfully reproduce their light refraction properties.

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Reconstructing Gas Flows Using Light-Path Approximation

Abstract: Transparent gas flows are difficult to reconstruct: the refractive index field (RIF)

Cited by 49 publications

References 26 publications

Light field distortion feature for transparent object classification

Light field distortion feature for transparent object classification

Linear Volumetric Focus for Light Field Cameras

Full 3D reconstruction of transparent objects

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