Next Best Light Position: A self configuring approach for the Reflectance Transformation Imaging acquisition process

Luxman, Ramamoorthy; Nurit, Marvin; Goïc, Gaëtan Le; Marzani, Franck; Mansouri, Alamin

doi:10.2352/issn.2470-1173.2021.5.maap-132

Cited by 5 publications

(7 citation statements)

References 9 publications

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“…The human–machine interface of LightBot is simple and completely built over a HTTP webserver. Hence, with the API of the LightBot, which gives access to various functionalities of the system, it is convenient to develop new methods in RTI acquisition, such as NBLP [ 15 ], HD-RTI [ 22 ], data stitching, etc. The key benefits of this system design are demonstrated in this paper.…”

Section: Discussionmentioning

confidence: 99%

“…LightBot accepts the standard Light Positions (.lp) file containing a predefined set of light directions and executes the acquisition by moving the light source oriented in the given directions. Moreover, the system, when combined with the Next Best Light Position [ 15 ], can be used as a standalone unit to perform fully automated RTI acquisition adjustable to the complexity of the object’s surface geometry. Solving the inverse kinematics problem of the system and collision avoidance are key to trajectory planning while executing the acquisition.…”

Section: Proposed Systemmentioning

confidence: 99%

“…Conventional RTI acquisitions are performed with homogeneously distributed fixed light positions, regardless of the size and complexity of the surface being acquired. Luxman et al [ 15 ] discuss the shortcomings of such acquisitions and their inadequacy in capturing the surface details. In order to fully realize the power of the RTI in revealing the surface phenomenon, it is important to choose the size of the virtual dome and the distribution of the light positions on it to be adaptive to the surface.…”

Section: Applicationsmentioning

confidence: 99%

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LightBot: A Multi-Light Position Robotic Acquisition System for Adaptive Capturing of Cultural Heritage Surfaces

et al. 2022

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Multi-light acquisitions and modeling are well-studied techniques for characterizing surface geometry, widely used in the cultural heritage field. Current systems that are used to perform this kind of acquisition are mainly free-form or dome-based. Both of them have constraints in terms of reproducibility, limitations on the size of objects being acquired, speed, and portability. This paper presents a novel robotic arm-based system design, which we call LightBot, as well as its applications in reflectance transformation imaging (RTI) in particular. The proposed model alleviates some of the limitations observed in the case of free-form or dome-based systems. It allows the automation and reproducibility of one or a series of acquisitions adapting to a given surface in two-dimensional space.

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Section: Discussionmentioning

confidence: 99%

Section: Proposed Systemmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

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LightBot: A Multi-Light Position Robotic Acquisition System for Adaptive Capturing of Cultural Heritage Surfaces

et al. 2022

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“…The principle of this technique is illustrated in Figure 1, where PTM, HSH, and DMD are RTI reconstruction models and stand for Polynomial Texture Mappings, Hemispherical Harmonics, and Discrete Modal Decomposition, respectively. Many new RTI acquisition modalities have recently been developed, including multispectral approaches [34,35], approaches to measure the complete luminance dynamic (HD-RTI, [36][37][38]) self-adaptive approaches to determine the relevant lighting directions (NBLP-RTI, [39]), and even robot-based RTI systems [40]. Within the framework of this research, we focus on the RTI acquisition parameters associated with the conventional approach.…”

Section: Reflectance Transformation Imagingmentioning

confidence: 99%

Reflectance Transformation Imaging as a Tool for Computer-Aided Visual Inspection

et al. 2022

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This work investigates the use of Reflectance Transformation Imaging (RTI) rendering for visual inspection. This imaging technique is being used more and more often for the inspection of the visual quality of manufactured surfaces. It allows reconstructing a dynamic virtual rendering of a surface from the acquisition of a sequence of images where only the illumination direction varies. We investigate, through psychometric experimentation, the influence of different essential parameters in the RTI approach, including modeling methods, the number of lighting positions and the measurement scale. In addition, to include the dynamic aspect of perception mechanisms in the methodology, the psychometric experiments are based on a design of experiments approach and conducted on reconstructed visual rendering videos. The proposed methodology is applied to different industrial surfaces. The results show that the RTI approach can be a relevant tool for computer-aided visual inspection. The proposed methodology makes it possible to objectively quantify the influence of RTI acquisition and processing factors on the perception of visual properties, and the results obtained show that their impact in terms of visual perception can be significant.

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“…Some images are more important than others. It is also observed that some regions in the hemisphere need more dense acquisition than others because reflectance is changing drastically in these regions [14].…”

mentioning

confidence: 99%

Can Surface Topography Give Us Best Light Positions for Reflectance Transformation Imaging?

Khawaja,

George,

Marzani

et al. 2023

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Reflectance Transformation Imaging (RTI) is a technique that provides an enhanced visualization experience. The current acquisition methods for Reflectance Transformation Imaging (RTI) are time-consuming and computationally expensive. This work investigates the idea of getting the best light positions for RTI acquisition using surface topography. We propose automating the RTI acquisition by estimating the surface topography using a deep learning method followed by estimating light positions using unsupervised clustering method. This is a one-shot method which only needs one image. We also created RTI Synthetic dataset in order to carry out experiments. We found that surface topography alone is not sufficient to estimate best light positions for RTI without putting constraints.

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Next Best Light Position: A self configuring approach for the Reflectance Transformation Imaging acquisition process

Abstract: Fast track article for IS&T International Symposium on Electronic Imaging 2021: Material Appearance 2021 proceedings.

Cited by 5 publications

References 9 publications

LightBot: A Multi-Light Position Robotic Acquisition System for Adaptive Capturing of Cultural Heritage Surfaces

LightBot: A Multi-Light Position Robotic Acquisition System for Adaptive Capturing of Cultural Heritage Surfaces

Reflectance Transformation Imaging as a Tool for Computer-Aided Visual Inspection

Can Surface Topography Give Us Best Light Positions for Reflectance Transformation Imaging?

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