A Differential Volumetric Approach to Multi-View Photometric Stereo

Logothetis, Fotios; Mecca, Roberto; Cipolla, Roberto

doi:10.1109/iccv.2019.00114

Cited by 41 publications

(22 citation statements)

References 41 publications

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“…Although the developments in RGB-D and other portable active sensors have led to success with volumetric methods on shading-based refinement [8,72,128], our work relies on image data for this problem. Much of the existing work that utilizes the images for complementary measurements uses explicit mathematical modeling for precise 3D geometry [34,83,65,69,66,88]. On the contrary, our work utilizes photometric stereo to estimate surface normals and then blends it with the shape volume density in neural radiance field representation to synthesize multi-view images and recover shapes.…”

Section: Photometric Stereo (Ps) Methodsmentioning

confidence: 99%

Neural Radiance Fields Approach to Deep Multi-View Photometric Stereo

Kaya¹,

Kumar²,

Sarno³

et al. 2022

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

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We present a modern solution to the multi-view photometric stereo problem (MVPS). Our work suitably exploits the image formation model in a MVPS experimental setup to recover the dense 3D reconstruction of an object from images. We procure the surface orientation using a photometric stereo (PS) image formation model and blend it with a multi-view neural radiance field representation to recover the object's surface geometry. Contrary to the previous multi-staged framework to MVPS, where the position, isodepth contours, or orientation measurements are estimated independently and then fused later, our method is simple to implement and realize. Our method performs neural rendering of multi-view images while utilizing surface normals estimated by a deep photometric stereo network. We render the MVPS images by considering the object's surface normals for each 3D sample point along the viewing direction rather than explicitly using the density gradient in the volume space via 3D occupancy information. We optimize the proposed neural radiance field representation for the MVPS setup efficiently using a fully connected deep network to recover the 3D geometry of an object. Extensive evaluation on the DiLiGenT-MV benchmark dataset shows that our method performs better than the approaches that perform only PS or only multi-view stereo (MVS) and provides comparable results against the state-of-the-art multistage fusion methods.

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Section: Photometric Stereo (Ps) Methodsmentioning

confidence: 99%

Neural Radiance Fields Approach to Deep Multi-View Photometric Stereo

Kaya¹,

Kumar²,

Sarno³

et al. 2022

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

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“…Although these systems can achieve high accuracy performances, the use of costly instrumentation limits the technique only to specialized laboratories and projects with unique metrological specifications. Multi-view stereo approaches were also used to create sparse 3D shape reconstruction, free of global deformation, of objects with Lambertian surfaces (Vlasic et al, 2009;Park et al, 2013;Grochulla et al, 2015;Park et al, 2016;Logothetis et al, 2019;Li et al, 2020;Xie et al, 2020). These were then used as a base for high-resolution measurements produced using photogrammetry.…”

Section: Combined Methodsmentioning

confidence: 99%

“…A common solution used to mitigate this issue is to increase the number of synchronized cameras (Vlasic et al, 2009;Xie et al, 2020), although it might be unsuitable for low-budget industrial inspection projects. Another alternative is to increase the number of image stations (Grochulla et al, 2015;Park et al, 2016;Logothetis et al, 2019), which is a time-consuming procedure, unsuitable for real-time 3D inspection. Li et al (2020), rather than rotating the camera around the object, used a turntable to rotate the object in order to capture multi-view images.…”

Section: Combined Methodsmentioning

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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“…local and non-strictly directional lighting) [14][15][16], more sophisticated surface reflectance models (i.e. "Bidirectional Reflectance Distribution Functions" or BRDF, other than the classical Lambertian reflectance) [17][18][19][20], the relaxation of constraints on camera placement [21][22][23] and combinations thereof [24]. Alongside these recent developments, Woodham's original formulation has been also extended from orthographic to perspective projection in [25].…”

Section: Motivation and Related Workmentioning

confidence: 99%

Photometric Stereo with Twin-Fisheye Cameras

Caracotte

Morbidi

Mouaddib

2021

2020 25th International Conference on Pattern Recognition (ICPR)

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Dans ce papier, nous introduisons et résolvons le problème de stéréophotométrie pour les caméras 360 • accessibles au grand public. Nous présentons une équation de l'irradiance pour les images sphériques, spécifiquement adaptée aux caméras twin-fisheye cameras ainsi qu'un algorithme pour l'estimation de la direction des éclairages, en s'appuyant sur la tache spéculaire qui apparait sur des sphères miroir. Plusieurs expérimentations sont réalisées à partir d'images de synthèse et d'images réelles acquises avec une caméra Ricoh Theta V, afin de vérifier le fonctionnement et la robustesse de l'approche proposée. Pour permettre et faciliter la reproduction des résultats, les jeux de données ainsi que les programmes développés sont publiquement disponibles à cette adresse : home.mis.u-picardie.fr/∼fabio/PhotoSphere.html Cet article a été accepté à l'International Conference on Pattern Recognition (ICPR) 2020.

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A Differential Volumetric Approach to Multi-View Photometric Stereo

Cited by 41 publications

References 41 publications

Neural Radiance Fields Approach to Deep Multi-View Photometric Stereo

Neural Radiance Fields Approach to Deep Multi-View Photometric Stereo

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

Photometric Stereo with Twin-Fisheye Cameras

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