Reconstruction of geometrical and reflection properties of surfaces by using structured light imaging technique

When a robust and dense surface reconstruction is aimed, structured light imaging techniques are usuallymuch appreciated. In this paper we propose a method to reconstruct both geometrical and reflective properties ofsurfaces by using structured light imaging. We use a technique where a camera and a projector are both treated asviewing devices. They are calibrated in the same manner. Each visible point can be correctly located on both imageplanes without solving a correspondence problem; hence, a dense reconstruction can be obtained. Since both the cameraand the projector are explicitly calibrated, lighting and viewing directions can be identified for each surface point. Itis also possible to measure reflected radiance by using high dynamic range (HDR) images for each surface point. Thelighting and viewing directions that are known after calibration are combined with the reflected radiance and the incomingirradiance measurements to determine the bidirectional reflectance distribution function (BRDF) values of the materialat the reconstructed surface points. We illustrate the reconstruction of surface reflection properties of sample surfacesby fitting the Phong BRDF model to the BRDF measurements.

PDF

___

Tippetts B, Lee DJ, Lillywhite K. Review of stereo vision algorithms and their suitability for resource-limited systems. J Real-Time Image Pr 2016; 11: 5-25.
Ozan Ş, Gümüştekin Ş. Calibration of double stripe 3d laser scanner systems using planarity and orthogonality constraints. Digit Signal Process 2014; 24: 231-243.
Zhang S, Huang PS. Novel method for structured light system calibration. Opt Eng 2006; 45: 1-8.
Geng J. Structured-light 3D surface imaging: a tutorial. Adv Opt Photonics 2011; 3: 128-160.
Wang ZZ, Yang YM. Single-shot three-dimensional reconstruction based on structured light line pattern. Opt Laser Eng 2018; 106: 10-16.
Vo M, Narasimhan SG, Sheikh Y, Texture illumination separation for single-shot structured light reconstruction. IEEE T Pattern Anal 2016; 38: 390-404.
Xu Y, Aliaga DG. Robust pixel classification for 3d modeling with structured light. In: Proceedings of Graph Interfaces; 2007. New York, NY, USA: ACM. pp. 233-240
Nayar SK, Krishnan G, Grossberg MD, Raskar R. Fast separation of direct and global components of a scene using high frequency illumination. ACM T Graphic 2006; 25: 935-944.
Zhang Z. A flexible new technique for camera calibration. IEEE T Pattern Anal 2000; 22: 1330-1334.
Moreno D, Taubin G. Simple, accurate, and robust projector-camera calibration. In: 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization Transmission; 2012; Zurich, Switzerland. New York, NY, USA: IEEE. pp. 464-471.
Debevec PE, Malik J. Recovering high dynamic range radiance maps from photographs. In: Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques; 2008; New York, NY, USA. pp. 369-378.
Matusik W. A data-driven reflectance model. PhD, Massachusetts Institute of Technology, Cambridge, MA, USA, 2003.
Ngan A, Durand F, Matusik W. Experimental analysis of BRDF models. Proceedings of the Sixteenth Eurographics Conference on Rendering Techniques; 2005; Switzerland. pp. 117-126.
Kurt M, Szirmay-Kalos L, Krivanek J. An anisotropic BRDF model for fitting and Monte Carlo rendering. Comp Graph 2010; 44: 1-15.
Ozan Ş. Joint reconstruction of surface geometry and reflection properties by using image based methods. PhD, İzmir Institute of Technology, İzmir, Turkey, 2015.
Mitra NJ, Nguyen A, Guibas LJ. Estimating surface normals in noisy point cloud data. Int J Comput Geom Ap 2004; 14: 261-276.
Pharr M, Humphreys G. Physically Based Rendering: From Theory to Implementation. 2nd ed. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2010.