Multi-view Inverse Rendering Under Arbitrary Illumination and Albedo

Kim, Ki-Chang; Torii, Akihiko; Okutomi, Masatoshi

doi:10.1007/978-3-319-46487-9_46

Cited by 45 publications

(53 citation statements)

References 47 publications

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“…Many methods like [28,39,21] use an optimisation framework to minimise a global energy. Interestingly, these methods implicitly use the vertex improvement iterations of Equation 5.…”

Section: As Shown Inmentioning

confidence: 99%

A Face Fairness Framework for 3D Meshes

Haque

Govindu

2019

Int J Comput Vis

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In this paper, we present a face fairness framework for 3D meshes that preserves the regular shape of faces and is applicable to a variety of 3D mesh restoration tasks. Specifically, we present a number of desirable properties for any mesh restoration method and show that our framework satisfies them. We then apply our framework to two different tasks -mesh-denoising and mesh-refinement, and present comparative results for these two tasks showing improvement over other relevant methods in the literature.

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“…Many methods like [28,39,21] use an optimisation framework to minimise a global energy. Interestingly, these methods implicitly use the vertex improvement iterations of Equation 5.…”

Section: As Shown Inmentioning

confidence: 99%

A Face Fairness Framework for 3D Meshes

Haque

Govindu

2019

Int J Comput Vis

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“…Kim et al . [KTO16] perform a shape refinement on images with different illuminations from the Internet and adopt per‐image SH to model the illumination. Zollöfer et al .…”

Section: Related Workmentioning

confidence: 99%

Shading‐Based Surface Recovery Using Subdivision‐Based Representation

Deng

Zheng

Cai

et al. 2018

Computer Graphics Forum

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This paper presents subdivision‐based representations for both lighting and geometry in shape‐from‐shading. A very recent shading‐based method introduced a per‐vertex overall illumination model for surface reconstruction, which has advantage of conveniently handling complicated lighting condition and avoiding explicit estimation of visibility and varied albedo. However, due to its discrete nature, the per‐vertex overall illumination requires a large amount of memory and lacks intrinsic coherence. To overcome these problems, in this paper we propose to use classic subdivision to define the basic smooth lighting function and surface, and introduce additional independent variables into the subdivision to adaptively model sharp changes of illumination and geometry. Compared to previous works, the new model not only preserves the merits of the per‐vertex illumination model, but also greatly reduces the number of variables required in surface recovery and intrinsically regularizes the illumination vectors and the surface. These features make the new model very suitable for multi‐view stereo surface reconstruction under general, unknown illumination condition. Particularly, a variational surface reconstruction method built upon the subdivision representations for lighting and geometry is developed. The experiments on both synthetic and real‐world data sets have demonstrated that the proposed method can achieve memory efficiency and improve surface detail recovery.

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“…For instance, the method in [5] iteratively refines the object surface using shading information, assuming that the albedo is constant or piecewise-constant [5]. This assumption was recently relaxed in [6,7], by resorting to properly designed priors on shape, shading and reflectance.…”

Section: Related Workmentioning

confidence: 99%

“…n, and the last one is a large n−1 i=1 (n − i)|Ω i | × N matrix used to represent the correspondence functions defined in Eq. (6). As for the non-null vector in the second row of Eq.…”

Section: Resolutionmentioning

confidence: 99%

Beyond Multi-view Stereo: Shading-Reflectance Decomposition

Mélou

Quéau

Durou

et al. 2017

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. Abstract. We introduce a variational framework for separating shading and reflectance from a series of images acquired under different angles, when the geometry has already been estimated by multi-view stereo.Our formulation uses an l 1 -TV variational framework, where a robust photometric-based data term enforces adequation to the images, total variation ensures piecewise-smoothness of the reflectance, and an additional multi-view consistency term is introduced for resolving the arising ambiguities. Optimisation is carried out using an alternating optimisation strategy building upon iteratively reweighted least-squares. Preliminary results on both a synthetic dataset, using various lighting and reflectance scenarios, and a real dataset, confirm the potential of the proposed approach.

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Multi-view Inverse Rendering Under Arbitrary Illumination and Albedo

Cited by 45 publications

References 47 publications

A Face Fairness Framework for 3D Meshes

A Face Fairness Framework for 3D Meshes

Shading‐Based Surface Recovery Using Subdivision‐Based Representation

Beyond Multi-view Stereo: Shading-Reflectance Decomposition

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