On controlling light transport in poor visibility environments

Gupta, Mohit; Narasimhan, Srinivasa G.; Schechner, Yoav Y.

doi:10.1109/cvpr.2008.4587763

Cited by 37 publications

(9 citation statements)

References 26 publications

(22 reference statements)

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“…For object classes 8 and 9, full global illumination effects have to be taken into account. Participating media can be made more transparent by employing controlled illumination [GNS08]. Opaque objects immersed in participating media can be acquired using structured light techniques [NNSK05, FHL * 08], and the scattering parameters of a homogeneous participating medium can be measured [NGD * 06, JDJ06].…”

Section: Figure 1: a Taxonomy Of Object Classes Based On Increasing Cmentioning

confidence: 99%

Transparent and Specular Object Reconstruction

Ihrke

Kutulakos²,

Lensch³

et al. 2010

Computer Graphics Forum

185

154

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This state of the art report covers reconstruction methods for transparent and specular objects or phenomena. While the 3D acquisition of opaque surfaces with lambertian reflectance is a well-studied problem, transparent, refractive, specular and potentially dynamic scenes pose challenging problems for acquisition systems. This report reviews and categorizes the literature in this field. Despite tremendous interest in object digitization, the acquisition of digital models of transparent or specular objects is far from being a solved problem. On the other hand, real-world data is in high demand for applications such as object modeling, preservation of historic artifacts and as input to data-driven modeling techniques. With this report we aim at providing a reference for and an introduction to the field of transparent and specular object reconstruction. We describe acquisition approaches for different classes of objects. Transparent objects/phenomena that do not change the straight ray geometry can be found foremost in natural phenomena. Refraction effects are usually small and can be considered negligible for these objects. Phenomena as diverse as fire, smoke, and interstellar nebulae can be modeled using a straight ray model of image formation. Refractive and specular surfaces on the other hand change the straight rays into usually piecewise linear ray paths, adding additional complexity to the reconstruction problem. Translucent objects exhibit significant sub-surface scattering effects rendering traditional acquisition approaches unstable. Different classes of techniques have been developed to deal with these problems and good reconstruction results can be achieved with current state-of-the-art techniques. However, the approaches are still specialized and targeted at very specific object classes. We classify the existing literature and hope to provide an entry point to this exiting field.

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Section: Figure 1: a Taxonomy Of Object Classes Based On Increasing Cmentioning

confidence: 99%

Transparent and Specular Object Reconstruction

Ihrke

Kutulakos²,

Lensch³

et al. 2010

Computer Graphics Forum

185

154

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“…We use the scattering model of [10], in a homogeneous medium. This model renders the imaged radiance in Eq.…”

Section: Simulationsmentioning

confidence: 99%

“…A straight path 40cm above a surface is set. The medium's parameters are β = 5[1/m], while the anisotropy parameter is g = 0.6 in the Henyey-Greenstein phase function [10,11]. C&L start from v(0).…”

Section: Simulationsmentioning

confidence: 99%

“…Scattering media degrades images. Studies aimed at enhancing visibility focus on single-image dehazing [8,12], or methods that modulate properties of the illumination, such as spatio-temporal structure [7,9,10,20,13], polarization [27,29]. However, all these methods do not exploit an important degree of freedom: the dynamic pose of the camera.…”

Section: Introductionmentioning

confidence: 99%

“…The challenge is exacerbated when lighting must be brought-in, in deep underwater operations, tissue and indoor smoky scenes. Scattering affects object irradiance and volumetric backscatter [10,14], as a function of the lighting pose, not only the camera pose ( Fig. 1).…”

Section: Introductionmentioning

confidence: 99%

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The Next Best Underwater View

Sheinin

Schechner

2016

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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To image in high resolution large and occlusion-prone scenes, a camera must move above and around. Degradation of visibility due to geometric occlusions and distances is exacerbated by scattering, when the scene is in a participating medium. Moreover, underwater and in other media, artificial lighting is needed. Overall, data quality depends on the observed surface, medium and the timevarying poses of the camera and light source (C&L). This work proposes to optimize C&L poses as they move, so that the surface is scanned efficiently and the descattered recovery has the highest quality. The work generalizes the next best view concept of robot vision to scattering media and cooperative movable lighting. It also extends descattering to platforms that move optimally. The optimization criterion is information gain, taken from information theory. We exploit the existence of a prior rough 3D model, since underwater such a model is routinely obtained using sonar. We demonstrate this principle in a scaled-down setup.

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