Shape from Shading

Shape-from-shading (SFS) is an important method to reconstruct three-dimensional (3D) shape of a surface in photometry and computer vision. Lambertian surface reflectance and orthographic camera projection are two fundamental assumptions which generally result in undesirable reconstructed results since inaccurate imaging model is adopted. In this paper, we propose a new fast 3D shape reconstruction approach via the SFS method relaxing the two assumptions. To this end, the Oren-Nayar reflectance and perspective projection models are used to establish an image irradiance equation which depicts the relationship between the 3D shape of non-Lambertian surfaces and its corresponding two-dimensional (2D) shading image. Considering the light attenuation of the near point source, the image irradiance equation is transformed into a static Hamilton-Jacobi partial differential equation (PDE) by solving a quadratic equation. The viscosity solution of the resultant Hamilton-Jacobi PDE is approximated by using optimal control theory and iterative fast marching method starting from a viscosity supersolution. The performance of the proposed approach is evaluated on both synthetic and real-world images and the experimental results demonstrate that the proposed approach is accurate and fast.

<p>This paper describes a shape from shading technique for the reconstruction of specular surfaces based on refraction and color illumination or multiple pulsed light sources. The technique is used to retrieve the small-scale shape of wave-undulated water surfaces in the laboratory and the field.</p>

In this research, we propose a novel algorithm which recovers the surface height directly from images obtained via perspective projection. Most existing shape from shading (SFS) algorithms are developed under the assumption of an orthogonal projection model, which is a special case of the general perspective projection model. As a result, there exist substantial reconstruction errors in their real applications especially for the case when the object is not far away from the camera. By introducing an explicit triangular element surface model and combining perspective imaging geometry, we are able to relax the orthogonal projection assumption and derive a more general reflectance model with perspective projection. We formulate the perspective SFS problem as a minimization problem parameterized by surface nodal heights, and determine the surface height by solving an equivalent linear system of equations. Simulation results for several test images are given to demonstrate the performance of the proposed algorithm. The proposed perspective SFS algorithm gives more accurate results than conventional SFS algorithms and is very attractive in practical applications.

Three-dimensional (3D) reconstruction and measurement are popular techniques in precision manufacturing processes. In this manuscript, a single image 3D reconstruction method is proposed based on a novel monocular vision system, which includes a three-level charge coupled device (3-CCD) camera and a ring structured multi-color light emitting diode (LED) illumination. Firstly, a procedure for the calibration of the illumination’s parameters, including LEDs’ mounted angles, distribution density and incident angles, is proposed. Secondly, the incident light information, the color distribution information and gray level information are extracted from the acquired image, and the 3D reconstruction model is built based on the camera imaging model. Thirdly, the surface height information of the detected object within the field of view is computed based on the built model. The proposed method aims at solving the uncertainty and the slow convergence issues arising in 3D surface topography reconstruction using current shape-from-shading (SFS) methods. Three-dimensional reconstruction experimental tests are carried out on convex, concave, angular surfaces and on a mobile subscriber identification module (SIM) card slot, showing relative errors less than 3.6%, respectively. Advantages of the proposed method include a reduced time for 3D surface reconstruction compared to other methods, demonstrating good suitability of the proposed method in reconstructing surface 3D morphology.

In this paper, we propose a novel approach for 3D surface reconstruction of the human jaw. Due to the difficulties of setting up a data acquisition system inside the mouth, we use an intra-oral camera to capture a sequence of calibrated images. These images are registered together to build a panoramic view of the jaw. We incorporate a shape from shading(SFS) algorithm that benefits from camera calibration parameters to build a 3D model from the panoramic image obtained from the previous stage. Our approach results in a 3D surface which has more fine details compared with those resulting from other literature techniques. We will demonstrate different artificial jaws surfaces reconstruction to show the efficiency of our system.

In this paper, the 3D surface reconstruction method of black melon seeds based on photometric stereo was studied, which was then used to identify the wrinkled seeds. After the image capturing system being set up, the system accuracy was calibrated by reconstructing a standard hemispherical object. Then, the whole 3D surface of a seed was built by reconstructing its central and periphery region with PS separately for the two regions are of great colour differences. Finally, experiments were done to show that cross surface area of the reconstructed surface can be used as wrinkle identification parameter with the identification threshold of 9. Under this threshold more than 90% of wrinkled seeds could be successfully identified while only 5% of normal seeds being identified as wrinkled.

IntroductionThe American Society of Gastroenterology Endoscopy led Preservation and Incorporation of Valuable Endoscopic Innovations initiative has identified real time polyp diagnosis as one of the next major technology-driven changes in...

In the objective research field of traditional Chinese medicine (TCM) tongue diagnosis, 2D tongue image is regarded as the research object for feature extraction and analysis in most current methods. However, 2D tongue image always can not visually express detail information of tongue 3D surface, such as lingual papilla, prick and fissure of tongue. It brings certain limitations to perceive real tongue surface. It is limited for the doctor to give accurate diagnosis. To solve this problem, a method to reconstruct the 3D surface of tongue based on photometric stereo is proposed. First, the surface normals and texture reflectance are obtained using photometric stereo technology in this paper, then calculate the depth values of tongue surface, and generate 3D depth map. It has been proved that the method is viable through lots of experiments and doctors may observe the 3D information of tongue surface from multiple perspectives. Detail information of tongue surface also may be expressed quantitatively and visually.

The reconstruction of a 3D surface through one gray scale digital image does not admit a unique solution in the orthographic Shape from Shading (SfS) framework. With the aim to make this type of problem well-posed it is possible to use the Photometric Stereo (PS) technique. It allows to add information about the surface introducing other images of the object taken from the same point of view but modifying, for each photo, the direction of the light source. The methods that use the PS technique with the orthographic model of SfS need of, at least, three images. However, even if three images are used, there is the possibility that the SfS-PS problem continues to be ill-posed. This is the case when the three images are taken using three coplanar light vectors. This work analyses this kind of ill-posedness in order to understand how it is possible to establish a connection among the images that do not guarantee uniqueness. A further result in this paper is given by a geometrical characterization of the surfaces for which it is possible to solve the classic SfS problem.

This paper presents a new method for recovering the shape of hybrid surfaces that have both diffuse reflection and specular reflection using shape from shading (SFS). The image irradiance equation has been derived as an explicit partial differential equation (PDE) under the assumptions of orthographic camera projection and distant point light source. The reflectance model of Ward has been used to express the hybrid reflection as a linear combination from the diffuse and specular components. The resulting PDE is solved using the Lax-Friedrichs sweeping method. The proposed algorithm is evaluated by using both synthetic data and real images and the experimental results show the efficiency of the approach.

Poster from:<br> V. B. S. Prasath, H. Kawanaka. Near-light perspective shape from shading for 3D visualizations in endoscopy systems. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas, MO, USA, Nov 13-16, 2017.

This paper presents an empirical study to investigate the use of photometric stereo (PS) for micro-scale 3D measurement of paper samples. PS estimates per-pixel surface orientation from images of a surface captured from the same viewpoint but under different illumination directions. Specifically, we investigate the surface properties of paper to test whether they are sufficiently well approximated by a Lambertian reflectance model to allow veridical surface reconstruction under PS and explore the range of conditions for which this model is valid. We present an empirical setup that is used to conduct a series of experiments in order to analyse the applicability of PS at the micro-scale. In addition, we determine the best 4, 6, and 8 light source tilt (illumination) angles with respect to multi-source micro-scale PS. Furthermore, an intensity based image registration method is used to test the accuracy of the recovery of surface normals. The results demonstrate that at the micro-scale: (a) Lambert model represents well the data sets with low root mean square (RMS) error between the original and reconstructed image, (b) increasing the light sources from 4 to 8 reduces RMS error, and (c) PS can be used to extract veridical surface normals.

Human observers estimate the illumination direction of rough surfaces rather precisely. When surfaces are rough, the illumination generates visible "texture" from differential shading at the level of the roughness, whereas differential illumination at the level of significant global surface curvature leads to the more familiar "shading". The shading is used in conventional shape-from-shading (SFS) algorithms, which ignore the illumination texture cue. Because of this simplification, SFS algorithms are typically formulated as global problems (partial differential equations, etc.). Human observers are likely to apply different methods than do these conventional SFS algorithms, however. When the roughness is not isotropic, one expects systematic errors in the visual detection of illumination direction, conceivably giving rise to erroneous shape estimates. Here we addressed this issue through systematic psychophysics on illumination direction detection as a function of the roughness anisotropy. Our expectations were fully borne out, in that the observers committed the predicted systematic errors. These results are precise enough to allow the inference that illumination direction detection is based on second-order statistics--that is, of edge detector (rather than line detector) activity.

Digital image-based 3D surface reconstruction is a streamlined and proper means of studying the features of the object being modelled. The generation of true 3D content is a very crucial step in any 3D system. A methodology to reconstruct a 3D surface of objects from a set of digital images is presented in this paper. It is simple, robust, and can be freely used for the construction of 3D surfaces from images. Digital images are taken as input to generate sparse and dense point clouds in 3D space from the detected and matched features. Poisson Surface, Ball Pivoting, and Alpha shape reconstruction algorithms have been used to reconstruct photo-realistic surfaces. Various parameters of these algorithms that are critical to the quality of reconstruction are identified and the effect of these parameters with varying values is studied. The results presented in this study will give readers an insight into the behaviour of various algorithmic parameters with computation time and fineness of reconstruction.

Photometric stereo surface reconstruction requires each input image to be associated with a particular 3D illumination vector. This signifies that the subject should be illuminated in turn by various directional illumination sources. In real life, this directionality may be reduced by ambient illumination, which is typically present as a diffuse component of the incident light. This work assesses the photometric stereo reconstruction quality for various ratios of ambient to directional illuminance and provides a reference for the robustness of photometric stereo with respect to that illuminance ratio. In our analysis, we focus on the face reconstruction application of photometric stereo, as faces are convex objects with rich surface variation, thus providing a suitable platform for photometric stereo reconstruction quality evaluation. Results demonstrate that photometric stereo renders realistic reconstructions of the given surface for ambient illuminance as high as nine times the illuminance of the directional light component.