Deep Near-Light Photometric Stereo for Spatially Varying Reflectances

Santo, Hiroaki; Waechter, Michael; Matsushita, Yasuyuki

doi:10.1007/978-3-030-58598-3_9

Cited by 19 publications

(48 citation statements)

References 25 publications

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“…The classical photometric stereo approaches work with perfectly diffuse (Lambertian) surfaces, which is often an improper assumption for many objects such as metallic, glossy, and shiny. In this regard, some researchers developed new methods that physically model how light interacts with the surface of non-Lambertian surfaces (Lu et al, 2013;Santo et al, 2020;Boss et al, 2020). Other works (MacDonald, 2014;MacDonald et al, 2016;Sun et al, 2017;Wei et al, 2018;Wen et al, 2021) classify and remove the specular highlights to deal with reflective surfaces.…”

Section: Photometric Stereomentioning

confidence: 99%

Investigating 3d Reconstruction of Non-Collaborative Surfaces Through Photogrammetry and Photometric Stereo

Karami

Menna

Remondino

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. 3D digital reconstruction techniques are extensively used for quality control purposes. Among them, photogrammetry and photometric stereo methods have been for a long time used with success in several application fields. However, generating highly-detailed and reliable micro-measurements of non-collaborative surfaces is still an open issue. In these cases, photogrammetry can provide accurate low-frequency 3D information, whereas it struggles to extract reliable high-frequency details. Conversely, photometric stereo can recover a very detailed surface topography, although global surface deformation is often present. In this paper, we present the preliminary results of an ongoing project aiming to combine photogrammetry and photometric stereo in a synergetic fusion of the two techniques. Particularly, hereafter, we introduce the main concept design behind an image acquisition system we developed to capture images from different positions and under different lighting conditions as required by photogrammetry and photometric stereo techniques. We show the benefit of such a combination through some experimental tests. The experiments showed that the proposed method recovers the surface topography at the same high-resolution achievable with photometric stereo while preserving the photogrammetric accuracy. Furthermore, we exploit light directionality and multiple light sources to improve the quality of dense image matching in poorly textured surfaces.

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Section: Photometric Stereomentioning

confidence: 99%

Investigating 3d Reconstruction of Non-Collaborative Surfaces Through Photogrammetry and Photometric Stereo

Karami

Menna

Remondino

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Near-field datasets: There are very limited, proper nearfield labeld data including a single object from [28] and 3 simple objects from [33].…”

Section: Ps Datasetsmentioning

confidence: 99%

“…However, despite the increased contribution from the computer vision community to tackle the near-field PS problem, the evaluation of such methods has relied on synthetic ( [17]) or very minimal real-world datasets [28,33]. The lack of shared data has prevented detailed and fair comparisons across the different methods.…”

Section: Introductionmentioning

confidence: 99%

LUCES: A Dataset for Near-Field Point Light Source Photometric Stereo

Mecca¹,

Logothetis²,

Budvytis³

et al. 2021

Preprint

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Three-dimensional reconstruction of objects from shading information is a challenging task in computer vision. As most of the approaches facing the Photometric Stereo problem use simplified far-field assumptions, real-world scenarios have essentially more complex physical effects that need to be handled for accurately reconstructing the 3D shape. An increasing number of methods have been proposed to address the problem when point light sources are assumed to be nearby the target object. The proximity of the light sources complicates the modeling of the image formation as the light behaviour requires non-linear parameterisation to describe its propagation and attenuation.To understand the capability of the approaches dealing with this near-field scenario, the literature till now has used synthetically rendered photometric images or minimal and very customised real-world data. In order to fill the gap in evaluating near-field photometric stereo methods, we introduce LUCES the first real-world 'dataset for near-fieLd point light soUrCe photomEtric Stereo' of 14 objects of a varying of materials. A device counting 52 LEDs has been designed to lit each object positioned 10 to 30 centimeters away from the camera. Together with the raw images, in order to evaluate the 3D reconstructions, the dataset includes both normal and depth maps for comparing different features of the retrieved 3D geometry. Furthermore, we evaluate the performance of the latest near-field Photometric Stereo algorithms on the proposed dataset to assess the SOTA method with respect to actual close range effects and object materials.

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“…Even objects with relatively constant emission spectra are difficult when material concentration varies [Sauer et al 2011;Zhang and Sato 2011]. Hybrid deep learning approaches combine near and far approximation lights to capture spatially varying reflectance [Santo et al 2020], but ignore effects of light penetration depth. Large volumes of data are required, and simple visible-only datasets are limiting.…”

Section: Object-scale Shapementioning

confidence: 99%