Exploration of Photometric Stereo Technology Applies to 3D Model Reconstruction (original) (raw)
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IJERT-Exploration of Photometric Stereo Technology Applies to 3D Model Reconstruction
International Journal of Engineering Research and Technology (IJERT), 2013
https://www.ijert.org/exploration-of-photometric-stereo-technology-applies-to-3d-model-reconstruction https://www.ijert.org/research/exploration-of-photometric-stereo-technology-applies-to-3d-model-reconstruction-IJERTV2IS120421.pdf An efficient method has been presented to achieve an accurate dense 3D reconstruction of objects using photometric stereo technology. The task of recovering three-dimensional geometry from two dimensional views of a scene is called 3D reconstruction. Photometric Stereo is a powerful image based 3D reconstruction technique that has recently been used to obtain very high quality reconstructions. The Photometric Stereo (PS) technique uses several images of the same surface taken from the same viewpoint but under illuminations with different directions. The illumination conditions refer to the light source direction and intensity, and reflectance properties mean what type of surface is under consideration i.e. Lambertian or non-Lambertian.. The algorithm has been tested on synthetic as well as real datasets and very encouraging results have been obtained.
Practical 3D Reconstruction Based on Photometric Stereo
Studies in Computational Intelligence, 2010
Photometric Stereo is a powerful image based 3d reconstruction technique that has recently been used to obtain very high quality reconstructions. However, in its classic form, Photometric Stereo suffers from two main limitations: Firstly, one needs to obtain images of the 3d scene under multiple different illuminations. As a result the 3d scene needs to remain static during illumination changes, which prohibits the reconstruction of deforming objects. Secondly, the images obtained must be from a single viewpoint. This leads to depth-map based 2.5 reconstructions, instead of full 3d surfaces. The aim of this chapter is to show how these limitations can be alleviated, leading to the derivation of two practical 3d acquisition systems: The first one, based on the powerful Coloured Light Photometric Stereo method can be used to reconstruct moving objects such as cloth or human faces. The second, permits the complete 3d reconstruction of challenging objects such as porcelain vases. In addition to algorithmic details, the chapter pays attention to practical issues such as setup calibration, detection and correction of self and cast shadows. We provide several evaluation experiments as well as reconstruction results.
A hand-held photometric stereo camera for 3-D modeling
2009 IEEE 12th International Conference on Computer Vision, 2009
This paper presents a simple yet practical 3-D modeling method for recovering surface shape and reflectance from a set of images. We attach a point light source to a hand-held camera to add a photometric constraint to the multi-view stereo problem. Using the photometric constraint, we simultaneously solve for shape, surface normal, and reflectance. Unlike prior approaches, we formulate the problem using realistic assumptions of a near light source, non-Lambertian surfaces, perspective camera model, and the presence of ambient lighting. The effectiveness of the proposed method is verified using simulated and real-world scenes.
A Comprehensive Introduction to Photometric 3D-Reconstruction
2020
Photometric 3D-reconstruction techniques aim at inferring the geometry of a scene from one or several images, by inverting a physical model describing the image formation. This chapter presents an introductory overview of the main pho-tometric 3D-reconstruction techniques which are shape-from-shading, photometric stereo and shape-from-polarisation.
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000
This paper addresses the problem of obtaining complete, detailed reconstructions of textureless shiny objects. We present an algorithm which uses silhouettes of the object, as well as images obtained under changing illumination conditions. In contrast with previous photometric stereo techniques, ours is not limited to a single viewpoint but produces accurate reconstructions in full 3D. A number of images of the object are obtained from multiple viewpoints, under varying lighting conditions. Starting from the silhouettes, the algorithm recovers camera motion and constructs the object's visual hull. This is then used to recover the illumination and initialize a multiview photometric stereo scheme to obtain a closed surface reconstruction. There are two main contributions in this paper: First, we describe a robust technique to estimate light directions and intensities and, second, we introduce a novel formulation of photometric stereo which combines multiple viewpoints and, hence, allows closed surface reconstructions. The algorithm has been implemented as a practical model acquisition system. Here, a quantitative evaluation of the algorithm on synthetic data is presented together with complete reconstructions of challenging real objects. Finally, we show experimentally how, even in the case of highly textured objects, this technique can greatly improve on correspondence-based multiview stereo results.
Fusing Multiview and Photometric Stereo for 3D Reconstruction under Uncalibrated Illumination
We propose a method to obtain a complete and accurate 3D model from multiview images captured under a variety of unknown illuminations. Based on recent results showing that for Lambertian objects, general illumination can be approximated well using low-order spherical harmonics, we develop a robust alternating approach to recover surface normals. Surface normals are initialized using a multi-illumination multiview stereo algorithm, then refined using a robust alternating optimization method based on the ' 1 metric. Erroneous normal estimates are detected using a shape prior. Finally, the computed normals are used to improve the preliminary 3D model. The reconstruction system achieves watertight and robust 3D reconstruction while neither requiring manual interactions nor imposing any constraints on the illumination. Experimental results on both real world and synthetic data show that the technique can acquire accurate 3D models for Lambertian surfaces, and even tolerates small violations of the Lambertian assumption.
Photometric Stereo for 3D Face Reconstruction Using Non Linear Illumination Models
Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction, 2017
Face recognition in presence of illumination changes, variant pose and different facial expressions is a challenging problem. In this paper, a method for 3D face reconstruction using photometric stereo and without knowing the illumination directions and facial expression is proposed in order to achieve improvement in face recognition. A dimensionality reduction method was introduced to represent the face deformations due to illumination variations and self shadows in a lower space. The obtained mapping function was used to determine the illumination direction of each input image and that direction was used to apply photometric stereo. Experiments with faces were performed in order to evaluate the performance of the proposed scheme. From the experiments it was shown that the proposed approach results very accurate 3D surfaces without knowing the light directions and with a very small differences compared to the case of known directions. As a result the proposed approach is more general and requires less restrictions enabling 3D face recognition methods to operate with less data.
Image and Vision Computing, 2007
This paper presents a new multi-light source photometric stereo system for reconstructing images of various characteristics of non-Lambertian rough surfaces with widely varying texture and specularity. Compared to the traditional three-light photometric stereo method, extra lights are employed using a hierarchical selection strategy to eliminate the effects of shadows and specularities, and to make the system more robust. We also show that six lights is the minimum needed in order to apply photometric stereo to the entire visible surface of any convex object. Experiments on synthetic and real scenes demonstrate that the proposed method can extract surface reflectance and orientation effectively, even in the presence of strong shadows and highlights. Hence, the method offers advantages in the recovery of dichromatic surfaces possessing rough texture or deeply relieved topographic features, with applications in reverse engineering and industrial surface inspection. Experimental results are presented in the paper. Published by Elsevier B.V.
International Journal of Computer Applications, 2014
When photometric stereo technique is used to recover shape and 3D information of an object from multiple images, it is common to assume that the light sources being used are collimated. When the light sources that are being used are actually point light sources, as in robot applications for weld seam inspection or underwater imaging, such an assumption causes significant error. The error increases further when the imaging system is deployed in an attenuating and scattering media. In such situations, a purely analytical solution is not possible. Current work proposes a novel iterative algorithm for recovering shape and 3D information in such situations.
An Application of the Photometric Stereo Method
1979
7' The orientation of patches on the surface of an object can be determined from multiple images taken with different illumination, but from the Sante viewing position. This method. refer red to as photometric stereo, can be implemented using table lookup based on nunwrica l inversion of experimentally determined reflectance maps. Here we concentrate on