A New Perspective on Uncalibrated Photometric Stereo

Papadhimitri, Thoma; Favaro, Paolo

doi:10.1109/cvpr.2013.194

Cited by 71 publications

(48 citation statements)

References 33 publications

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“…To our knowledge, this very general radiance model has not been used in the context of photometric stereo so far. In most applications, a single point light source at infinity is assumed, so that each lighting is represented by a constant vector in R 3 [17,7,1,14,15]. Closer to this general expression is the important work of Basri et al in [2], where a distribution of point light sources at infinity is assumed, allowing the authors to develop shape recovery algorithms based on low order spherical harmonic decompositions.…”

Section: Image Formation Modelmentioning

confidence: 99%

“…Extending photometric stereo to more general models has become an important research direction, along with the natural need for fast and robust solutions. For example, perspective projection can replace the orthographic projection in both calibrated [11] and uncalibrated [14] cases, the Lambertian assumption can be relaxed so as to take specular highlights into account [7] and robustness to outliers like shadows can be obtained through numerous techniques such as sparse regression [10]. However, extending photometric stereo to non-directional lightings remains an open and challenging problem.…”

Section: Introductionmentioning

confidence: 99%

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A $$L^1$$-TV Algorithm for Robust Perspective Photometric Stereo with Spatially-Varying Lightings

Quéau

Lauze

Durou

2015

Lecture Notes in Computer Science

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 15234The contribution was presented at SSVM 2015 :http://ssvm2015.math.u-bordeaux.fr/ Abstract. We tackle the problem of perspective 3D-reconstruction of Lambertian surfaces through photometric stereo, in the presence of outliers to Lambert's law, depth discontinuities, and unknown spatiallyvarying lightings. To this purpose, we introduce a robust L 1 -TV variational formulation of the recovery problem where the shape itself is the main unknown, which naturally enforces integrability and permits to avoid integrating the normal field.

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Section: Image Formation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A $$L^1$$-TV Algorithm for Robust Perspective Photometric Stereo with Spatially-Varying Lightings

Quéau

Lauze

Durou

2015

Lecture Notes in Computer Science

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“…The first method computes the normal field and then modifies the gradient field based on the perspective projection, as also proposed in Refs. [44,45]. As it manipulates normal vectors, we refer to this technique as the perspective projection based on normal field (PPN) method.…”

Section: Perspective Projectionmentioning

confidence: 99%

Photometric stereo for strong specular highlights

2018

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Photometric stereo is a fundamental technique in computer vision known to produce 3D shape with high accuracy. It uses several input images of a static scene taken from one and the same camera position but under varying illumination. The vast majority of studies in this 3D reconstruction method assume orthographic projection for the camera model. In addition, they mainly use the Lambertian reflectance model as the way that light scatters at surfaces. Thus, providing reliable photometric stereo results from real world objects still remains a challenging task. We address 3D reconstruction by use of a more realistic set of assumptions, combining for the first time the complete Blinn-Phong reflectance model and perspective projection. Furthermore, we compare two different methods of incorporating the perspective projection into our model. Experiments are performed on both synthetic and real world images; the latter do not benefit from laboratory conditions. The results show the high potential of our method even for complex real world applications such as medical endoscopy images which may include many specular highlights.

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“…Several additional assumptions (on the geometry, the albedo and/or the lights) need to be imposed to the problem to fix the GBR ambiguity. This can be done by exploiting diffuse maxima [20], specular maxima [13], interreflections [9], entropy minimization [4,23], chromaticity clustering [26] or perspective projection [21].…”

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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A New Perspective on Uncalibrated Photometric Stereo

Cited by 71 publications

References 33 publications

A $$L^1$$-TV Algorithm for Robust Perspective Photometric Stereo with Spatially-Varying Lightings

A $$L^1$$-TV Algorithm for Robust Perspective Photometric Stereo with Spatially-Varying Lightings

Photometric stereo for strong specular highlights

Uncalibrated Near-Light Photometric Stereo

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