Photometric stereo for strong specular highlights

Khanian, Maryam; Boroujerdi, Ali Sharifi; Breuß, Michael

doi:10.1007/s41095-017-0101-9

Cited by 16 publications

(16 citation statements)

References 57 publications

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“…In the future, we are going to combine techniques as detail‐preserved surface reconstruction, 29 texture mapping, 30,31 illumination decomposition, 32 bidirectional reflectance distribution function reconstruction, 33 and MVS for specular highlights 34 to reconstruct more realistic 3D models.…”

Section: Resultsmentioning

confidence: 99%

Dense multiview stereo based on image texture enhancement

Liao

Wei

et al. 2020

Computer Animation & Virtual

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In this paper, we propose a novel Multiview Stereo (MVS) method which can effectively estimate geometry in low‐textured regions. Conventional MVS algorithms predict geometry by performing dense correspondence estimation across multiple views under the constraint of epipolar geometry. As low‐textured regions contain less feature information for reliable matching, estimating geometry for low‐textured regions remains hard work for previous MVS methods. To address this issue, we propose an MVS method based on texture enhancement. By enhancing texture information for each input image via our multiscale bilateral decomposition and reconstruction algorithm, our method can estimate reliable geometry for low‐textured regions that are intractable for previous MVS methods. To densify the final output point cloud, we further propose a novel selective joint bilateral propagation filter, which can effectively propagate reliable geometry estimation to neighboring unpredicted regions. We validate the effectiveness of our method on the ETH3D benchmark. Quantitative and qualitative comparisons demonstrate that our method can significantly improve the quality of reconstruction in low‐textured regions.

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Section: Resultsmentioning

confidence: 99%

Dense multiview stereo based on image texture enhancement

Liao

Wei

et al. 2020

Computer Animation & Virtual

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“…The Blinn–Phong model is an empirical light reflectance model that integrates diffuse reflection and specular reflection [ 22 ], and it is also studied for the photometric stereo problem of non-ideal Lambertian surfaces. Reference [ 23 ] combined the Blinn–Phong reflectance model and perspective projection to solve the 3D reconstruction problem; nevertheless, the infinite point light source assumption limits its usage for more general applications. Reference [ 24 ] integrated the Blinn–Phong reflectance model with the close point light source model; by performing iteration from one ground truth value of the surface depth, the surface depth map can be reconstructed.…”

Section: Related Workmentioning

confidence: 99%

Photometric Stereo-Based Depth Map Reconstruction for Monocular Capsule Endoscopy

Yang

Meng

et al. 2020

Sensors

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The capsule endoscopy robot can only use monocular vision due to the dimensional limit. To improve the depth perception of the monocular capsule endoscopy robot, this paper proposes a photometric stereo-based depth map reconstruction method. First, based on the characteristics of the capsule endoscopy robot system, a photometric stereo framework is established. Then, by combining the specular property and Lambertian property of the object surface, the depth of the specular highlight point is estimated, and the depth map of the whole object surface is reconstructed by a forward upwind scheme. To evaluate the precision of the depth estimation of the specular highlight region and the depth map reconstruction of the object surface, simulations and experiments are implemented with synthetic images and pig colon tissue, respectively. The results of the simulations and experiments show that the proposed method provides good precision for depth map reconstruction in monocular capsule endoscopy.

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“…As to complete our brief review of some general aspects of PS, let us note that it is possible to extend Woodham's classic PS model as for instance to non-Lambertian reflectance as e.g. in [2,14,16,21,37], or to take into account several types of lighting in a scene, see e.g. [3,31].…”

Section: Arxiv:170910437v1 [Cscv] 29 Sep 2017mentioning

confidence: 99%

“…Observe that, if A(z, ρ) and b(z, ρ) were constant, problem (19) would be a linear least squares problem with respect to z. Let us remark that (19) can be easily extended to include more realistic reflectance [15,16] and lighting [28,31] models, as well as more robust estimators [14,33]: this only requires to change the definition of f , which stands for the global reprojection error E R .…”

Section: Discretisationmentioning

confidence: 99%

Optimisation of Classic Photometric Stereo by Non-convex Variational Minimisation

et al. 2018

Self Cite

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Estimating shape and appearance of a three dimensional object from a given set of images is a classic research topic that is still actively pursued. Among the various techniques available, photometric stereo is distinguished by the assumption that the underlying input images are taken from the same point of view but under different lighting conditions. The most common techniques provide the shape information in terms of surface normals. In this work, we instead propose to minimise a much more natural objective function, namely the reprojection error in terms of depth. Minimising the resulting non-trivial variational model for photometric stereo allows to recover the depth of the photographed scene directly. As a solving strategy, we follow an approach based on a recently published optimisation scheme for non-convex and non-smooth cost functions.The main contributions of our paper are of theoretical nature. A technical novelty in our framework is the usage of matrix differential calculus. We supplement our approach by a detailed convergence analysis of the resulting optimisation algorithm and discuss possibilities to ease the computational complexity. At hand of an experimental evaluation we discuss important properties of the method. Overall, our strategy achieves more accurate results than competing approaches. The experiments also highlights some practical aspects of the underlying optimisation algorithm that may be of interest in a more general context. G. Radow · L. Hoeltgen · M. Breuß Chair for Applied Mathematics

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Photometric stereo for strong specular highlights

Cited by 16 publications

References 57 publications

Dense multiview stereo based on image texture enhancement

Dense multiview stereo based on image texture enhancement

Photometric Stereo-Based Depth Map Reconstruction for Monocular Capsule Endoscopy

Optimisation of Classic Photometric Stereo by Non-convex Variational Minimisation

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