Linear Differential Constraints for Photo-Polarimetric Height Estimation

Tozza, Silvia; Smith, William A. P.; Zhu, Dizhong; Ramamoorthi, Ravi; Hancock, Edwin R.

doi:10.1109/iccv.2017.250

Cited by 36 publications

(19 citation statements)

References 32 publications

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“…This requirement was afterwards relaxed in [112], where spatially-varying albedo was estimated from a single polarisation image, assuming known illumination and strong smoothness assumptions. In [120] variants of the aforementioned method have been exploited by introducing additional constraints which arise when a second light source is considered, allowing to relax the uniform albedo assumption even under unknown lighting. In this work, albedo-invariant or phase-invariant formulations were proposed.…”

Section: Polarisation and Shading Cuesmentioning

confidence: 99%

A Comprehensive Introduction to Photometric 3D-Reconstruction

Durou

Falcone

Quéau

et al. 2020

Advances in Computer Vision and Pattern Recognition

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Section: Polarisation and Shading Cuesmentioning

confidence: 99%

A Comprehensive Introduction to Photometric 3D-Reconstruction

Durou

Falcone

Quéau

et al. 2020

Advances in Computer Vision and Pattern Recognition

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“…Atkinson [2] combine calibrated two source photometric stereo with polarisation phase and resolve ambiguities via a region growing process. Tozza et al [29] generalised [28] to consider two source photo-polarimetric shape estimation. Subsequently, Mecca et al [18] also proposed a differential formulation with a well-posed solution for two light sources.…”

Section: Related Workmentioning

confidence: 99%

“…Polarisation is a particularly attrac-tive shape estimation cue because it is dense (surface orientation information is available at every pixel), can be applied to smooth, featureless, glossy surfaces (on which multiview methods would fail to find correspondences) and it can be captured in a single shot (using a polarisation camera). For this reason, the shape-from-polarisation cue has recently been rediscovered and significant progress has been made in the past three years [2,7,9,15,16,18,24,28,29,34].…”

Section: Introductionmentioning

confidence: 99%

Depth From a Polarisation + RGB Stereo Pair

Zhu

Smith

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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Estimated specular mask Corrected normal Depth from stereo (a) Setup and Input (b) Local disambiguation Estimated Albedo Estimated Shape (c) Output Section 4 Section 5 Section 6 Figure 1: Overview: From a stereo pair of one polarisation image and one RGB image (a) we merge stereo depth with polarisation normals using a higher order graphical model (b) before estimating an albedo map and the final geometry (c). AbstractIn this paper, we propose a hybrid depth imaging system in which a polarisation camera is augmented by a second image from a standard digital camera. For this modest increase in equipment complexity over conventional shapefrom-polarisation, we obtain a number of benefits that enable us to overcome longstanding problems with the polarisation shape cue. The stereo cue provides a depth map which, although coarse, is metrically accurate. This is used as a guide surface for disambiguation of the polarisation surface normal estimates using a higher order graphical model. In turn, these are used to estimate diffuse albedo. By extending a previous shape-from-polarisation method to the perspective case, we show how to compute dense, detailed maps of absolute depth, while retaining a linear formulation. We show that our hybrid method is able to recover dense 3D geometry that is superior to state-of-the-art shape-from-polarisation or two view stereo alone.

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“…The non-linear redundant spatial information has been simplified by considering image ratio leading to homogeneous linear PDEs that describe the geometry of the object via its level-set. After that, other approaches based on image ratio have been presented (Tozza et al 2017;Yu et al 2017) assuming diffuse reflection only and considering two-light uncalibrated photometric stereo data and smoothness/convexity priors respectively, for computing the 3D shape.…”

Section: Previous Workmentioning

confidence: 99%

A Differential Approach to Shape from Polarisation: A Level-Set Characterisation

et al. 2018

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Despite the longtime research aimed at retrieving geometrical information of an object from polarimetric imaging, physical limitations in the polarisation phenomena constrain current approaches to provide ambiguous depth estimation. As an additional constraint, polarimetric imaging formulation differs when light is reflected off the object specularly or diffusively. This introduces another source of ambiguity that current formulations cannot overcome. With the aim of deriving a formulation capable of dealing with as many heterogeneous effects as possible, we propose a differential formulation of the Shape from Polarisation problem that depends only on polarimetric images. This allows the direct geometrical characterisation of the level-set of the object keeping consistent mathematical formulation for diffuse and specular reflection. We show via synthetic and real-world experiments that diffuse and specular reflection can be easily distinguished in order to extract meaningful geometrical features from just polarimetric imaging. The inherent ambiguity of the Shape from Polarization problem becomes evident through the impossibility of reconstructing the whole surface with this differential approach. To overcome this limitation, we consider shading information elegantly embedding this new formulation into a two-light calibrated photometric stereo approach.

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Linear Differential Constraints for Photo-Polarimetric Height Estimation

Abstract: In this paper we present a differential approach to photopolarimetric

Cited by 36 publications

References 32 publications

A Comprehensive Introduction to Photometric 3D-Reconstruction

A Comprehensive Introduction to Photometric 3D-Reconstruction

Depth From a Polarisation + RGB Stereo Pair

A Differential Approach to Shape from Polarisation: A Level-Set Characterisation

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