Near Light Correction for Image Relighting and 3D Shape Recovery

In Proceedings of the First French-British …, 2000

Computer augmented reality is a rapidly emerging field allowing users to mix virtual and real worlds. Our interest is to allow relighting and remodelling of real scenes, using a reflectance estimation method. Most previous work focused on the quality of the results without considering the expense in computation and the price of acquisition equipment. In this paper, we present a low–cost photometric calibration method which improves the reflectance estimate of real scenes. This is achieved by adapting high-dynamic range ...

Journal of Mathematical Imaging and Vision, 2017

We conduct a thorough study of photometric stereo under nearby point light source illumination, from modeling to numerical solution, through calibration. In the classical formulation of photometric stereo, the luminous fluxes are assumed to be directional, which is very difficult to achieve in practice. Rather, we use light-emitting diodes (LEDs) to illuminate the scene to be reconstructed. Such point light sources are very convenient to use, yet they yield a more complex photometric stereo model which is arduous to solve. We first derive in a physically sound manner this model, and show how to calibrate its parameters. Then, we discuss two state-of-the-art numerical solutions. The first

2013

Publication in the conference proceedings of EUSIPCO, Marrakech, Morocco, 2013

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015

Figure . Given an existing 3D scene (left), our method builds approximate object models from image fragments (two teapots and an ostrich) and inserts them in the scene (right). Lighting and shadowing of the inserted objects appear consistent with the rest of the scene. Our method also captures complex reflection (front teapot), refraction (back teapot) and depth-of-field of effects (ostrich). None of these rendering effects can be achieved by image-based editing tools, while our hybrid approach avoids the pain of accurate 3D or material modeling.

Twelfth International Conference on Quality Control by Artificial Vision 2015, 2015

Among the possible sources of error in 3D-reconstruction using the photometric stereo technique, lighting modelling is often neglected, though it can create a dramatic large-scale bias. In this paper, after recalling the physical definition of a primary Lambertian source (isotropic lightings), we show how to derive a lighting model for several real-world scenarios, including directional lightings, nearby sources and extended planar illuminants. Finally, we show how to calibrate general spatially-varying lightings within a plane, in the case where explicitely modelling the lightings would be tedious.

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.

International Journal of Computer Vision, 2006

Work on photometric stereo has shown how to recover the shape and reflectance properties of an object using multiple images taken with a fixed viewpoint and variable lighting conditions. This work has primarily relied on the presence of a single point source of light in each image. In this paper we show how to perform photometric stereo assuming that all lights in a scene are isotropic and distant from the object but otherwise unconstrained. Lighting in each image may be an unknown and arbitrary combination of diffuse, point and extended sources. Our work is based on recent results showing that for Lambertian objects, general lighting conditions can be represented using low order spherical harmonics. Using this representation we can recover shape by performing a simple optimization in a low-dimensional space. We also analyze the shape ambiguities that arise in such a representation.

International Journal of Computer Theory and Engineering, 2013

To implement the photometric stereo technique, the radiance distribution of the respective light sources from the different illumination directions must be accurately known. Most previous work has tended to assume distance point sources, so that a collimated and uniform illumination distribution can be approximated, thereby allowing the photometric stereo problem to be easily solved in a linear way. However, there can be significant practical difficulties in realizing such idealized light sources in real world applications. In addition, the strategy of using distant light sources produces a low signal/noise ratio for the system, and is also unsuitable for applications where setup space is limited. These problems potentially limit new opportunities for the wider applications of photometric stereo beyond the research laboratory in evolving areas such as industrial inspection, security and medical applications. This paper proposes a compensation method for illumination radiance to allow the possibility of employing normal low-cost commercial light sources. A flat diffuse surface with either homogeneous or heterogeneous albedo distribution is used to sample the radiance distribution before implementing photometric stereo. The unevenly distributed light radiance is eliminated by using the acquired reference information. The experimental results demonstrate the efficacy of the proposed method.

Journal of Mathematical Imaging and Vision, 2016

Recovering the 3D shape of an object from shading is a challenging problem due to the complexity of modeling light propagation and surface reflections. Photometric Stereo (PS) is broadly considered a suitable approach for high-resolution shape recovery, but its functionality is restricted to a limited set of object surfaces and controlled lighting setup. In particular, PS models generally consider reflection from objects as purely diffuse, with specularities being regarded as a nuisance that breaks down shape reconstruction. This is a serious drawback for implementing PS approaches since most common materials have prominent specular components. In this paper, we propose a PS model that solves the problem for both diffuse and specular components aimed at shape recovery of generic objects with the approach being independent of the albedo values

2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008

We present a recursive algorithm for 3D surface reconstruction based on Photometric Stereo in the presence of highlights, and self and cast shadows. We assume that the surface reflectance outside the highlights can be approximated by the Lambertian model. The algorithm works with as few as three light sources, and it can be generalised for N without any difficulties. Furthermore, this reconstruction method is able to identify areas where the majority of the lighting directions result in unreliable pixel intensities, providing the capability to adjust a reconstruction algorithm and improve its performance avoiding the unreliable sources. We report results for both artificial and real images and compare them with the results of other state of the art photometric stereo algorithms.