3D reconstruction of mirror-type objects using efficient ray coding

Tin, Siu-Kei; Ye, Jinwei; Nezamabadi, Mahdi; Chen, Can

doi:10.1109/iccphot.2016.7492867

Cited by 10 publications

(8 citation statements)

References 37 publications

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“…The technique consists in measuring the reflected ray by decoding two world positions for every pixel. Approaches based on triangulation [5] , [6] and polarization [7] , [8] have been demonstrated. By the computation of the direction of light rays, this technique employs the principle of light field.…”

Section: A Deflectometrymentioning

confidence: 99%

“…In the field of deflectometry, this conceptualization of illumination leads to the direct ray measurements technique: it generally uses two planes which are crossed by rays of a light source, and the reconstruction is parameterized by the direction and the intensity of the reflected ray. [8] , [7] Furthermore, on the basis of this plenoptic illumination model, Herschbach developed an optical system generating a structured light field from an array of light sources by means of refracting or reflecting light structuring element [16]. This optical system is interesting since the optical formulas are well-known making the spacing between light rays calculable.…”

Section: B Light Fieldmentioning

confidence: 99%

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3D reconstruction of mirror-like surfaces by smart deflectometry

Poirier-Herbeck

Aubreton

Fofi

2021

Fifteenth International Conference on Quality Control by Artificial Vision

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Specular and mirror-like surfaces are difficult to reconstruct by the fact that light of the surrounding environment is reflected off the surface. Nowadays, it is still a challenge in many application fields to get a fast and accurate reconstruction of specular objects. 3D acquisition by deflectometry involves estimating the 3D profile of a specular surface by analyzing the image of a light source or a pattern captured by a camera after reflection on the surface. These techniques require the knowledge of the location and the orientation of the camera and the orientation of the light source. If camera calibration is a process widely known and controlled and guaranteeing good accuracy, estimating the orientation of the light source is still an issue. We propose to overcome it by using a light source whose the color changes according to the orientation. Indeed, we implement a vision system of light source's position recognition by the color in simulation with the 3D computer graphics software Blender, we define a calibration technique and we suggest some possible light sources to export our simulated vision system to reality. Plus, we explore the potential benefit of light field in order to model such a system.

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Section: A Deflectometrymentioning

confidence: 99%

Section: B Light Fieldmentioning

confidence: 99%

3D reconstruction of mirror-like surfaces by smart deflectometry

Poirier-Herbeck

Aubreton

Fofi

2021

Fifteenth International Conference on Quality Control by Artificial Vision

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show abstract

“…The first are focused on non-diffuse surfaces, while the others aim at reconstructing weakly supported surfaces of any kind. Tin et al [26] adopt a two-layer liquid crystal display (LCD) setup to encode the illumination directions for reconstruction of the mirror-type specular objects. Or-El et al [57] address the same issue by exploiting the built-in monochromatic IR projector and IR images of RGB-D scanners.…”

Section: B Weakly Supported Surface Reconstructionmentioning

confidence: 99%

“…Most of the methods reported in the literature concentrate on flossy surface reconstruction and address the problem by adding extra hardware (e.g., coded pattern projection [25] and a two-layer LCD [26]), filtering the non-Lambert region [27], [28] or translating multi-view images of the objects with specular reflection to diffuse images [29]. Although these methods have made great progress towards non-diffuse surface reconstruction, they cannot handle textureless images, making them unable to realize general weakly supported surface reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Weakly Supported Plane Surface Reconstruction via Plane Segmentation Guided Point Cloud Enhancement

et al. 2020

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Most of the widely used multi-view 3D reconstruction algorithms assume that object appearance is predominantly diffuse and full of good texture. For the objects that violate this restriction, the surface can hardly be reconstructed because such area lacks sufficient support from dense point clouds. To tackle this problem, we introduce a novel two-stage prior-guided method based on point clouds enhancement to enable the application of multi-view reconstruction approaches in such scenes. In the first stage, we optimize the original PlaneNet plane segmentation priors by taking advantage of the estimated depth map and confidence map from multi-view stereo. In the second stage, we correct and supply 3D point clouds for the weakly supported plane surface on the basis of the upgraded priors. Furthermore, we utilize a slight disturbance of the enhanced point clouds to facilitate the subsequent mesh reconstruction. The proposed point cloud enhancement approach is evaluated on the large-scale DTU dataset. Our method significantly outperforms previous multi-view stereo state-of-the-arts. We also demonstrate weakly supported plane surface reconstruction results from real-world photos that are unachievable with either the methods aiming at preserving weakly supported surfaces or the traditional state-of-the-art 3D reconstruction systems.

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“…These techniques, however, make rather strong assumptions about the data, mainly that target objects are predominantly diffuse with almost no specular reflectance. Multi-view reconstruction of glossy surfaces is a challenging problem, which has been addressed by adding specialized hardware (e.g., coded pattern projection [3] and two-layer LCD [4]), imposing surface constraints [5,6], or making use of additional information like silhouettes and environment maps [7], or the Blinn-Phong model [8]. Fig.…”

Section: Introductionmentioning

confidence: 99%

Specular-to-Diffuse Translation for Multi-view Reconstruction

Huang

Portenier

et al. 2018

Computer Vision – ECCV 2018

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Most multi-view 3D reconstruction algorithms, especially when shapefrom-shading cues are used, assume that object appearance is predominantly diffuse. To alleviate this restriction, we introduce S2Dnet, a generative adversarial network for transferring multiple views of objects with specular reflection into diffuse ones, so that multi-view reconstruction methods can be applied more effectively. Our network extends unsupervised image-to-image translation to multiview "specular to diffuse" translation. To preserve object appearance across multiple views, we introduce a Multi-View Coherence loss (MVC) that evaluates the similarity and faithfulness of local patches after the view-transformation. Our MVC loss ensures that the similarity of local correspondences among multi-view images is preserved under the image-to-image translation. As a result, our network yields significantly better results than several single-view baseline techniques. In addition, we carefully design and generate a large synthetic training data set using physically-based rendering. During testing, our network takes only the raw glossy images as input, without extra information such as segmentation masks or lighting estimation. Results demonstrate that multi-view reconstruction can be significantly improved using the images filtered by our network. We also show promising performance on real world training and testing data.

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3D reconstruction of mirror-type objects using efficient ray coding

Cited by 10 publications

References 37 publications

3D reconstruction of mirror-like surfaces by smart deflectometry

3D reconstruction of mirror-like surfaces by smart deflectometry

Weakly Supported Plane Surface Reconstruction via Plane Segmentation Guided Point Cloud Enhancement

Specular-to-Diffuse Translation for Multi-view Reconstruction

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