Robust 3D Projective Reconstruction by Learning

Shu, Mao; Hu, Lihua; Dong, Qiulei; Xu, Huarong; Hu, Zhanyi

doi:10.3724/sp.j.1089.2018.16251

Cited by 1 publication

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“…However, due to the special properties of transparent materials, it is a very complex problem to obtain complete surface geometry information about them. The material distribution inside a transparent object can be highly inconsistent, 1 and in addition the reflective properties of transparent materials are quite different compared to non-transparent materials.…”

Section: Introductionmentioning

confidence: 99%

Polarized 3D reconstruction for transparent objects based on reflection type separation method

Deng,

Zhao

2024

Speckle 2023: VIII International Conference on Speckle Metrology

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Polarization imaging technique as a simple, fast and low-cost 3D reconstruction method has many applications in the field of 3D reconstruction technology. The principle of polarization 3D reconstruction technique is based on Fresnel's theorem in physical optics, which uses the relationship between the polarization information of the image and the surface normal of the object to reconstruct the object. Since different types of reflections on an object's surface correspond to different polarization information and surface normal, this can lead to significant deviations for transparent objects with a variety of reflection types. Therefore, we used a method that separates the partially polarized diffuse and specular reflection components by applying a polarized industrial camera and a standard industrial camera to acquire images and combining them with a depth estimation method based on polarized images to generate a depth distribution for the surface of a transparent object with a known reflectivity. Non-transparent objects were chosen for technical validation as well as transparent objects for experimental analysis. The obtained results show that compared with the polarized 3D reconstruction method that neglects partially polarized diffuse reflections, this method can reconstruct the surface of transparent objects with sufficient accuracy and has good utility in relevant industrial production.

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Section: Introductionmentioning

confidence: 99%