A Microfacet-Based Model for Photometric Stereo with General Isotropic Reflectance

Chen, Lixiong; Zheng, Yinqiang; Shi, Boxin; Subpa-asa, Art; Sato, Iwao

doi:10.1109/tpami.2019.2927909

Cited by 14 publications

(4 citation statements)

References 53 publications

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“…Rather than rejecting the specularities and shadow regions as outliers, sophisticated models were developed to approximate all observed pixels. These reflectance models employ sophisticated polynomial functions to approximate the real-world materials, such as the bivariate functions [25]- [28], Ward reflectance model [5], [29], the specular spike reflectance model [7], [30], Blinn-Phong reflectance model [31], Torrance-Sparrow reflectance model [32], and the microfacet models [33]. However, these handcrafted analytic models are useful only for limited classes of non-Lambertian surfaces because the reflectance models vary dramatically from material to material.…”

Section: A Photometric Stereomentioning

confidence: 99%

Recovering Surface Normal and Arbitrary Images: A Dual Regression Network for Photometric Stereo

Dong

Chen

2021

IEEE Trans. on Image Process.

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Section: A Photometric Stereomentioning

confidence: 99%

Recovering Surface Normal and Arbitrary Images: A Dual Regression Network for Photometric Stereo

Dong

Chen

2021

IEEE Trans. on Image Process.

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“…They do this instead of discarding or separating non-Lambertian reflections. Micro-facet based BRDF models, including the Torrance-Sparrow model [ 50 ] and the isotropic Ward model [ 51 ], are considered for handling the reflectance property of an object’s glossy surface [ 23 , 25 , 26 , 27 , 52 ]. The bi-polynomial reflectance model has been proposed to represent the low-frequency non-Lambertian reflectance component [ 24 ].…”

Section: Related Workmentioning

confidence: 99%

“…Several BRDFs have been developed to model real-world material surfaces. The use of analytical and empirical BRDF models has been popular due to their compact representations, and these models are employed to cope with the complex reflection of an object in a photometric stereo [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. However, this approach has some drawbacks that limit their accuracy and applicability.…”

Section: Introductionmentioning

confidence: 99%

Deep Photometric Stereo Network with Multi-Scale Feature Aggregation

Lee

2020

Sensors

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We present photometric stereo algorithms robust to non-Lambertian reflection, which are based on a convolutional neural network in which surface normals of objects with complex geometry and surface reflectance are estimated from a given set of an arbitrary number of images. These images are taken from the same viewpoint under different directional illumination conditions. The proposed method focuses on surface normal estimation, where multi-scale feature aggregation is proposed to obtain a more accurate surface normal, and max pooling is adopted to obtain an intermediate order-agnostic representation in the photometric stereo scenario. The proposed multi-scale feature aggregation scheme using feature concatenation is easily incorporated into existing photometric stereo network architectures. Our experiments were performed with a DiLiGent photometric stereo benchmark dataset consisting of ten real objects, and they demonstrated that the accuracies of our calibrated and uncalibrated photometric stereo approaches were improved over those of baseline methods. In particular, our experiments also demonstrated that our uncalibrated photometric stereo outperformed the state-of-the-art method. Our work is the first to consider the multi-scale feature aggregation in photometric stereo, and we showed that our proposed multi-scale fusion scheme estimated the surface normal accurately and was beneficial to improving performance.

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“…Such a simple least-squares approach may be replaced by robust variational or learning-based strategies to ensure robustness [13,17,33,34]. There also exist numerical solutions for handling non-Lambertian reflectance models [7,15,21,40], non-distant light sources [18,22,29,32], or the ill-posed cases where m = 2 [16,20,24,31] or m = 1 [5,8,39,45]. Such issues are not adressed in the present paper which rather focuses on the theoretical foundations of uncalibrated photometric stereo.…”

Section: Calibrated Photometric Stereo Under Directional Lightingmentioning

confidence: 99%

On the well-posedness of uncalibrated photometric stereo under general lighting

Brahimi¹,

Quéau²,

Haefner³

et al. 2019

Preprint

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Uncalibrated photometric stereo aims at estimating the 3D-shape of a surface, given a set of images captured from the same viewing angle, but under unknown, varying illumination. While the theoretical foundations of this inverse problem under directional lighting are well-established, there is a lack of mathematical evidence for the uniqueness of a solution under general lighting. On the other hand, stable and accurate heuristical solutions of uncalibrated photometric stereo under such general lighting have recently been proposed. The quality of the results demonstrated therein tends to indicate that the problem may actually be well-posed, but this still has to be established. The present paper addresses this theoretical issue, considering first-order spherical harmonics approximation of general lighting. Two important theoretical results are established. First, the orthographic integrability constraint ensures uniqueness of a solution up to a global concave-convex ambiguity, which had already been conjectured, yet not proven. Second, the perspective integrability constraint makes the problem well-posed, which generalizes a previous result limited to directional lighting. Eventually, a closed-form expression for the unique least-squares solution of the problem under perspective projection is provided, allowing numerical simulations on synthetic data to empirically validate our findings.

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A Microfacet-Based Model for Photometric Stereo with General Isotropic Reflectance

Cited by 14 publications

References 53 publications

Recovering Surface Normal and Arbitrary Images: A Dual Regression Network for Photometric Stereo

Recovering Surface Normal and Arbitrary Images: A Dual Regression Network for Photometric Stereo

Deep Photometric Stereo Network with Multi-Scale Feature Aggregation

On the well-posedness of uncalibrated photometric stereo under general lighting

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