Towards Full 3D Helmholtz Stereovision Algorithms

Delaunoy, Amaël; Prados, Emmanuel; Belhumeur, Peter N.

doi:10.1007/978-3-642-19315-6_4

Cited by 11 publications

(6 citation statements)

References 21 publications

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“…It remains, however, necessary to swap the light and camera for each pair. Delaunoy et al [2010] use the same concept as Zickler et al [2002] but formulate an energy over a global 3D model instead of separate depth maps.…”

Section: Invariantsmentioning

confidence: 99%

A Survey of Photometric Stereo Techniques

Ackermann

Goesele

2015

FNT in Computer Graphics and Vision

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Reconstructing the shape of an object from images is an important problem in computer vision that has led to a variety of solution strategies. This survey covers photometric stereo, i.e., techniques that exploit the observed intensity variations caused by illumination changes to recover the orientation of the surface. In the most basic setting, a diffuse surface is illuminated from at least three directions and captured with a static camera. Under some conditions, this allows to recover per-pixel surface normals. Modern approaches generalize photometric stereo in various ways, e.g., relaxing constraints on lighting, surface reflectance and camera placement or creating different types of local surface estimates.Starting with an introduction for readers unfamiliar with the subject, we discuss the foundations of this field of research. We then summarize important trends and developments that emerged in the last three decades. We put a focus on approaches with the potential to be applied in a broad range of scenarios. This implies, e.g., simple capture setups, relaxed model assumptions, and increased robustness requirements. The goal of this review is to provide an overview of the diverse concepts and ideas on the way towards more general techniques than traditional photometric stereo.

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Section: Invariantsmentioning

confidence: 99%

A Survey of Photometric Stereo Techniques

Ackermann

Goesele

2015

FNT in Computer Graphics and Vision

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“…In the recent work by Roubtsova and Guillemaut [8] Bayesian formulation of HS with a tailored prior jointly optimising depth and normal information is shown to produce superior results to the original ML formulation in [6]. There has also been some work [11], [12] on full 3D reconstruction by HS.…”

Section: Related Workmentioning

confidence: 99%

Colour Helmholtz Stereopsis for Reconstruction of Complex Dynamic Scenes

Roubtsova

Guillemaut

2014

2014 2nd International Conference on 3D Vision

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Abstract-Helmholtz Stereopsis (HS) is a powerful technique for reconstruction of scenes with arbitrary reflectance properties. However, previous formulations have been limited to static objects due to the requirement to sequentially capture reciprocal image pairs (i.e. two images with the camera and light source positions mutually interchanged). In this paper, we propose colour HS -a novel variant of the technique based on wavelength multiplexing. To address the new set of challenges introduced by multispectral data acquisition, the proposed novel pipeline for colour HS uniquely combines a tailored photometric calibration for multiple camera/light source pairs, a novel procedure for surface chromaticity calibration and the state-of-the-art Bayesian HS suitable for reconstruction from a minimal number of reciprocal pairs. Experimental results including quantitative and qualitative evaluation demonstrate that the method is suitable for flexible (single-shot) reconstruction of static scenes and reconstruction of dynamic scenes with complex surface reflectance properties.

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“…The work of Delaunoy et al (Delaunoy et al, 2010) and the more recent publication of Weinmann et al (Weinmann et al, 2012) are two notable examples. Both are variational approaches requiring computationally intensive optimisation over the entire surface with long execution times.…”

Section: Related Workmentioning

confidence: 99%

A Bayesian Framework for Enhanced Geometric Reconstruction of Complex Objects by Helmholtz Stereopsis

Roubtsova

Guillemaut

2014

Proceedings of the 9th International Conference on Computer Vision Theory and Applications

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Abstract:Helmholtz stereopsis is an advanced 3D reconstruction technique for objects with arbitrary reflectance properties that uniquely characterises surface points by both depth and normal. Traditionally, in Helmholtz stereopsis consistency of depth and normal estimates is assumed rather than explicitly enforced. Furthermore, conventional Helmholtz stereopsis performs maximum likelihood depth estimation without neighbourhood consideration. In this paper, we demonstrate that reconstruction accuracy of Helmholtz stereopsis can be greatly enhanced by formulating depth estimation as a Bayesian maximum a posteriori probability problem. In reformulating the problem we introduce neighbourhood support by formulating and comparing three priors: a depth-based, a normal-based and a novel depth-normal consistency enforcing one. Relative performance evaluation of the three priors against standard maximum likelihood Helmholtz stereopsis is performed on both real and synthetic data to facilitate both qualitative and quantitative assessment of reconstruction accuracy. Observed superior performance of our depth-normal consistency prior indicates a previously unexplored advantage in joint optimisation of depth and normal estimates.

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Towards Full 3D Helmholtz Stereovision Algorithms

Cited by 11 publications

References 21 publications

A Survey of Photometric Stereo Techniques

A Survey of Photometric Stereo Techniques

Colour Helmholtz Stereopsis for Reconstruction of Complex Dynamic Scenes

A Bayesian Framework for Enhanced Geometric Reconstruction of Complex Objects by Helmholtz Stereopsis

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