Handheld Reflectance Acquisition of Paintings

Thanikachalam, Niranjan; Baboulaz, Loïc; Firmenich, Damien; Süsstrunk, Sabine; Vetterli, Martin

doi:10.1109/tci.2017.2749182

Cited by 3 publications

(4 citation statements)

References 26 publications

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“…The separation of the specular component has been proposed as well by Macdonald [Mac18] as a way to improve the quality of CH object relighting. The importance of the specular reconstruction for the visualization of relighted paintings' surface is demonstrated also by Thanikachalam et al [TBF∗17], where the method based on the reconstruction of light transport matrix with a compressive sensing approach (Sec. 5.3) preserves the visual richness of paintings, and allows for the visualization of how the specular behaviour migrates over the painting's surface.…”

Section: Methodsmentioning

confidence: 96%

“…Specifically, they interpolate a discretized matrix j) called Light Transport Matrix (LTM), which represents the light reflected at each pixel location as a function of the illumination on a planar surface parallel to the image plane. Thanikachalam et al [TBF∗17] sample the matrix entries by acquiring the object under varying illumination with a mobile phone LED that moves on a plane, and the relighting is performed with a compressive‐sensing based reconstruction that has been proved to interpolate better than bicubic interpolation or kernel regression. Similarly, the LTM can be also recovered from a sparse sampling with the use of a Neural Network ensemble, by exploiting spatial coherence [RDL∗15].…”

Section: Methodsmentioning

confidence: 99%

“…In terms of capture, to avoid hallucinations, very high‐frequency MLIC should be employed. Moreover, the size of typical reconstructed light transport function for each pixel is about 200k samples [TBF∗17], so, even for small images, the representation tends to pose very hard problems in terms of storage, transmission, and computation required to meet interactive rates during relighting. The use of neural networks [RDL∗15], trained for each specific dataset, tries to cope with this problem, by producing a relatively small model with tractable memory footprint and fast relighting performance.…”

Section: Methodsmentioning

confidence: 99%

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State‐of‐the‐art in Multi‐Light Image Collections for Surface Visualization and Analysis

Pintus

Dulecha

Ciortan

et al. 2019

Computer Graphics Forum

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Multi‐Light Image Collections (MLICs), i.e., stacks of photos of a scene acquired with a fixed viewpoint and a varying surface illumination, provide large amounts of visual and geometric information. In this survey, we provide an up‐to‐date integrative view of MLICs as a mean to gain insight on objects through the analysis and visualization of the acquired data. After a general overview of MLICs capturing and storage, we focus on the main approaches to produce representations usable for visualization and analysis. In this context, we first discuss methods for direct exploration of the raw data. We then summarize approaches that strive to emphasize shape and material details by fusing all acquisitions in a single enhanced image. Subsequently, we focus on approaches that produce relightable images through intermediate representations. This can be done both by fitting various analytic forms of the light transform function, or by locally estimating the parameters of physically plausible models of shape and reflectance and using them for visualization and analysis. We finally review techniques that improve object understanding by using illustrative approaches to enhance relightable models, or by extracting features and derived maps. We also review how these methods are applied in several, main application domains, and what are the available tools to perform MLIC visualization and analysis. We finally point out relevant research issues, analyze research trends, and offer guidelines for practical applications.

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Section: Methodsmentioning

confidence: 96%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

State‐of‐the‐art in Multi‐Light Image Collections for Surface Visualization and Analysis

Pintus

Dulecha

Ciortan

et al. 2019

Computer Graphics Forum

View full text Add to dashboard Cite

show abstract

“…They take a short video of a target object along with the BRDF chart while moving the hand-held light tube, and recover SVBRDFs from a linear combination of the reference BRDFs. Last, Thanikachalam et al [2017] present an acquisition setup similar to Won et al 's [2012], focusing on capturing reflectance of planar art paintings.…”

Section: Related Workmentioning

confidence: 99%

Practical SVBRDF acquisition of 3D objects with unstructured flash photography

et al. 2018

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Capturing spatially-varying bidirectional reflectance distribution functions (SVBRDFs) of 3D objects with just a single, hand-held camera (such as an off-the-shelf smartphone or a DSLR camera) is a difficult, open problem. Previous works are either limited to planar geometry, or rely on previously scanned 3D geometry, thus limiting their practicality. There are several technical challenges that need to be overcome: First, the built-in flash of a camera is almost colocated with the lens, and at a fixed position; this severely hampers sampling procedures in the light-view space. Moreover, the near-field flash lights the object partially and unevenly. In terms of geometry, existing multiview stereo techniques assume diffuse reflectance only, which leads to overly smoothed 3D reconstructions, as we show in this paper. We present a simple yet powerful framework that removes the need for expensive, dedicated hardware, enabling practical acquisition of SVBRDF information from real-world, 3D objects with a single, off-the-shelf camera with a built-in flash. In addition, by removing the diffuse reflection assumption and leveraging instead such SVBRDF information, our method outputs high-quality 3D geometry reconstructions, including more accurate high-frequency details than state-of-the-art multiview stereo techniques. We formulate the joint reconstruction of SVBRDFs, shading normals, and 3D geometry as a multi-stage, iterative inverse-rendering reconstruction pipeline. Our method is also directly applicable to any existing multiview 3D reconstruction technique. We present results of captured objects with complex geometry and reflectance; we also validate our method numerically against other existing approaches that rely on dedicated hardware, additional sources of information, or both.

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