A simple strategy for calibrating the geometry of light sources

Powell, Mark W.; Sarkar, Sudeep; Goldgof, Dmitry

doi:10.1109/34.955114

Cited by 86 publications

(43 citation statements)

References 22 publications

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“…While this complete system is capable of representing the effects of point light sources, the irradiance is computed by interpolation on the bases of those accurate ones at cameras' projection centers and it is still difficult to achieve estimation of light sources that are in proximal scene. To our knowledge the most closely related work to our proposition in this respect is that by Powell et al [12]. They present a technique for calibrating the light source geometry by matching highlights on three spheres.…”

Section: Introductionmentioning

confidence: 99%

Difference sphere: An approach to near light source estimation

Takai¹,

Maki²,

Niinuma³

et al. 2009

Computer Vision and Image Understanding

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Section: Introductionmentioning

confidence: 99%

Difference sphere: An approach to near light source estimation

Takai¹,

Maki²,

Niinuma³

et al. 2009

Computer Vision and Image Understanding

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“…There are also techniques focusing on particular practical scenarios such as laparoscopy [11] or noise reduction [19]. Typically, the calibration is done by using at least two calibration spheres [22] and the 3D position of lights is calculated by triangulating at least two rays in space. This procedure is very sensitive to noise and measurement errors.…”

Section: Prior Workmentioning

confidence: 99%

Uncalibrated Near-Light Photometric Stereo

Papadhimitri¹,

Favaro²

2014

Proceedings of the British Machine Vision Conference 2014

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In this work we solve the uncalibrated photometric stereo problem with lights placed near the scene. Although the devised model is more complex than its far-light counterpart, we show that under a global linear ambiguity the reconstruction is possible up to a rotation and scaling, which can be easily fixed. We also propose a solution for reconstructing the normal map, the albedo, the light positions and the light intensities of a scene given only a sequence of near-light images. This is done in an alternating minimization framework which first estimates both the normals and the albedo, and then the light positions and intensities. We validate our method on real world experiments and show that a near-light model leads to a significant improvement in the surface reconstruction compared to the classic distant illumination case.

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“…This is the case especially when the proximity of light source is to be considered, i.e., the closer a light source is to the occluding object, the larger portion of shadow is likely to be cast by the object, which causes such instability as previously discussed. Although a few recent advances [3,9,13,2] allow estimation of near light source, it is still a challenging problem to solve for parameters of a complex lighting environment.…”

Section: Introductionmentioning

confidence: 99%

Self shadows and cast shadows in estimating illumination distribution

Takai¹,

Maki²,

Matsuyama³

2007

IET 4th European Conference on Visual Media Production (CVMP 2007)

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This paper compares two methods for estimating illumination distributions from shadows: methods using self shadows and cast shadows. The cast shadow method provides an effective framework for recovering illuminations in a real scene utilizing variations of image brightnesses inside shadows cast by an occluding object of known shape. The shadow surface is typically the same plane on which the object is placed. A new self shadow method is proposed in this paper in which the shadow surface is on the occluding object itself. We show that it works even for lighting environments where the cast shadow method fails while clarifying the stability issues related to sampling resolution, and the selection of lighting model. We also present an automatic framework for estimation of near lighting by a depth identification, as well as the directional analysis of the illumination distribution, which was not addressed in the cast shadow method. We prove the effectiveness of our new method with experiments using real scenes.

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A simple strategy for calibrating the geometry of light sources

Cited by 86 publications

References 22 publications

Difference sphere: An approach to near light source estimation

Difference sphere: An approach to near light source estimation

Uncalibrated Near-Light Photometric Stereo

Self shadows and cast shadows in estimating illumination distribution

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