A Single-Pixel Imaging Based Stereo Deflectometry for 3D Reconstruction of Free-Form Transparent Objects With Parasitic Reflections

The measurement of transparent objects has always been regarded as one of the difficult problems in 3D reconstruction technology. For transparent objects conforming to the birefringence model, we propose a method for measuring the parameters of transparent objects based on multi-wavelength incident light. This paper analyzes the transparent objects where light enters the birefringence model. Because the transparent objects refract light of different wavelengths differently, when the lights with different wavelengths emitted from the same point and same direction passes through the transparent objects, the position and direction of light with different wavelength will come out differently. The position and direction of these outgoing rays are related to the refractive index and surface topography of the transparent objects. By obtaining the equations of these outgoing rays, we can derive the equations of the refractive index and surface topography of the transparent objects. In this paper, the incident light obtained by computer simulation is used as the initial data, and the simulated annealing algorithm is used to verify the model. The final results show that within the range of random error values given by the computer, the surface shape, curvature, thickness and refractive index of transparent objects with small error values can be obtained. At the same time, this paper introduces the Ceres Solver C++ nonlinear optimization library into the simulated annealing algorithm, which improves the speed and accuracy of the algorithm and greatly enhances the robustness of the algorithm.

The name "FRINGE-CONTRAST DITERFEROMETRY" /FCl/ is proposed for such measuring techniques and interpretation methods which are based on information contained in the fringe-visibility distribution in interference images. In general, the fringe-visibility distribution depends on kind and direction of object illumination, shape and microstructure of object surface, such parameters of imaging system as focusing distance, aperture size, as well as shape and orientation of diaphragm, and - on differences between two states of object surface being compared using an interferometry technique. The early papers on this subject have concerned the case of imaging system focused exactly on the object surface, with circularly [1,2,3] and arbitrarily Du shaped diaphragm. In this case, the fringe visibility depends on the differences described only by the displacement components perpendicular to viewing and/or illumination directions. The recent works [5,6,7, and unpublished yet] have been based on the general equations describing the fringe visibility formed by imaging system focused on the object plane which is arbitrarily situated with respect to the object surface. These equations allow to apply FCI to arbitrarily-shaped object surfaces for automated determination of the shape of object surface, the fringe-order distribution, 3-D dis-placement field, as well as the surface-strain and rotation tensor fields - directly from fringe-contrast measurement, without differentiating the displacement field.

Phase shifting profilometry is an important technique for reconstructing the three-dimensional (3D) geometry of objects with purely diffuse surfaces. However, it is challenging to measure the transparent objects due to the pattern aliasing caused by light refraction and multiple reflections inside the object. In this work, we analyze the aliasing fringe pattern formation for transparent objects and then, propose to learn the front surface light intensity distribution based on the formation principle by using the diffusion models for generating the non-aliased fringe patterns reflected from the front surface only. With the generated fringe patterns, the 3D shape of the transparent objects can be reconstructed via the conventional structured light. We show the feasibility and performance of the proposed method on the data of purely transparent objects that are not seen in the training stage. Moreover, we found it could be generalized to other cases with local-transparent and translucent objects, showing the potential capability of the diffusion based learnable framework in tackling the problems of transparent object reconstruction.

Today, with quality becoming increasingly important, each product requires three-dimensional in-line quality control. On the other hand, the 3D reconstruction of transparent objects is a very difficult problem in computer vision due to transparency and specularity of the surface. This paper proposes a new method, called Scanning From Heating (SFH), to determine the surface shape of transparent objects using laser surface heating and thermal imaging. Furthermore, the application to transparent glass is discussed and results on different surface shapes are presented.

A favoured sensor for mapping is a 3D laser scanner since it allows a wide scanning range, precise measurements, and is usable indoor and outdoor. Hence, a mapping module delivers detailed and high resolution maps which makes it possible to navigate safely. Difficulties result from transparent and specular reflective objects which cause erroneous and dubious measurements. At such objects, based on the incident angle, measurements result from the object surface, an object behind the transparent surface, or an object mirrored with respect to the reflective surface. This paper describes an enhanced Pre-Filter-Module to distinguish between these cases. Two experiments demonstrate the usability and show that for single scans the identification of mentioned objects in 3D is possible. The first experiment was made in an empty room with a mirror. The second experiment was made in a stairway which contains a glass door. Further, results show that a discrimination between a specular reflective and a transparent object is possible. Especially for transparent objects the detected size is restricted to the incident angle. That is why future work concentrates on implementing a post-filter module. Gained experience shows that collecting the data of multiple scans and postprocess them as soon as the object was bypassed will improve the map.

Single-pixel imaging commonly uses a spatial light modulator (SLM) to encode a scene's spatial information into a one-dimensional light signal sequence so that a single-pixel detector can be used to capture a scene. Digital micromirror device, liquid crystal on silicon, or light emitted diode matrix is a common choice of SLM, but it requires a certain lens system in order to project the structured light pattern that is generated by the SLM onto the scene. Using a lens would not only lead to aberration but also result in difficulty for establishing a compact imaging system. Therefore, we propose to use a liquid crystal display (LCD) as an intensity-only SLM to conduct structured illumination. As such, single-pixel imaging can be performed in a lensless way. As an instance of the proposed technique, a small-size and multi-functional scanner is designed and established to prove the lensless single-pixel imaging concept. As experimentally demonstrated, the single-pixel scanner can not only achieve grayscale and true-color scanning as a typical scanner does, but also achieve distinctive functionalities, such as accurate optical character recognition from under-sampled data, on-the-fly encryption, and genuine document identification. This compact scanner is as thin as 2.48 millimeters. The proposed lensless single-pixel imaging technique might find applications in various fields.

12 This paper deals with the non-contact optical sensor to measure the shape and the thickness of transparent plates such as glass panel of LCD (Liquid Crystal Display). The conventional methods to obtain the shape or thickness of transparent plates are contact type sensor such as LVDT (Linear Variable Differential Transformer). Due to the contact between the tip of the sensor and the surface of objects, the tip is abraded. In addition the casting glass under high temperature results in extending the size of sensor body. The accuracy of the sensor is degraded due to these reasons. In this paper, to overcome these problems, we proposed a low cost non-contact optical sensor that is composed of a hologram laser unit used for optical pickup of CD player and a plastic lens. To evaluate the performance of the proposed optical sensor, a series of experiments were performed for various measurement conditions. Based upon observation of the experimental results, the proposed sensor shows a good performance in measuring the shape of transparent plates.

This paper considers the segmentation of range image measurements into surface patches which are either plane or curved and which are described formally by a function. After a formal description of the segmentation, we present and compare three methods suited for plane and curved patch segmentation and show the results of experiments conducted for testing their practical behaviour. The two first methods use the classical approach of region growing whereas the third method is based on a relaxation process. This original and last method exhibits simplicity and low computational complexity. Thanks to its parallel nature, it can be considered as a good candidate for range image segmentation in real-time applications.

The practical, rapid, and accurate optical 3D reconstruction of transparent objects with contemporary non-contact optical techniques, has been an open challenge in the field of optical metrology. The combination of refraction, reflection, and transmission in transparent objects makes it very hard to use common off-the-shelf 3D reconstruction solutions to accurately reconstruct transparent objects in three dimensions without completely coating the object with an opaque material. We demonstrate in this work that a specific class of transparent objects can indeed be reconstructed without the use of opaque spray coatings, via Optical Projection Tomography (OPT). Particularly, the 3D reconstruction of large thin-walled hollow transparent objects can be achieved via OPT, without the use of refractive-index-matching liquid, accurately enough for use in both cultural heritage and beverage packaging industry applications. We compare 3D reconstructions of our proposed OPT method to those achieved by an industrial-grade 3D scanner and report average shape differences of ±0.34 mm for 'shelled' hollow objects and ±0.92 mm for 'non-shelled' hollow objects. A disadvantage of using OPT, which was noticed on the thicker 'non-shelled' hollow objects, as opposed to the 'shelled' hollow objects, was that it induced partial filling of hollow areas and the deformation of embossed features.

In this paper, we propose possibility for reconstruction of surface of an underwater object or 3D scene reconstruction of an underwater environment using an economical RGB-D sensor such as Microsoft Kinect. Reconstructing the 3D surface of an underwater object is a challenging task due to degraded quality of underwater images. There are various reasons of quality degradation of underwater images i.e., non-uniform illumination of light on the surface of objects, scattering and absorption effects. Particles and impurities present in underwater produces Gaussian noise on the captured underwater optical images which degrades the quality of images. However, using depth sensors, as a cost effective alternative, we aim to show that underwater 3D scene reconstruction is possible with sight tradeoffs on accuracy but major cost saving. The acquired depth data is proposed to be processed by applying real-time mesh generating techniques from the acquired point cloud. The experimental result aims to show that the proposed method reconstructs 3D surface of underwater objects accurately using captured underwater depth images.

Due to medium scattering, absorption, and complex light interactions, capturing objects from the underwater environment has always been a difficult task. Single-pixel imaging (SPI) is an efficient imaging approach that can obtain spatial object information under low-light conditions. In this paper, we propose a single-pixel object inspection system for the underwater environment based on compressive sensing super-resolution convolutional neural network (CS-SRCNN). With the CS-SRCNN algorithm, image reconstruction can be achieved with 30% of the total pixels in the image. We also investigate the impact of compression ratios on underwater object SPI reconstruction performance. In addition, we analyzed the effect of peak signal to noise ratio (PSNR) and structural similarity index (SSIM) to determine the image quality of the reconstructed image. Our work is compared to the SPI system and SRCNN method to demonstrate its efficiency in capturing object results from an underwater environment. The PSNR and SSIM of the proposed method have increased to 35.44% and 73.07%, respectively. This work provides new insight into SPI applications and creates a better alternative for underwater optical object imaging to achieve good quality.

The visual 3D reconstruction of transparent objects in unstructured scenes is challenging due to the complex image formation principles underlying their visual appearance. Most state-of-the-art reconstruction methods ignore this problem and assume Lambertian reflection. Yet, transparent objects are relevant scene information for applications in intelligent robotics (such as grasping) or virtual reality. In this work, we present an approach to detect non-planar transparent objects, like bottles or glasses, by specifically searching for geometry inconsistencies caused by refraction or reflection. Depth information is acquired using a Kinect sensor, which is moved within the scene in order to acquire multiple views. The individual measurements are combined into a 3D volume, yielding the objects' location and a rough shape estimate. Results are presented using various household objects made of glass or plastic.

This paper examines the consequences of the inversion of refelcted waves from an object based on the Helmholtz wave equation model; the primary wave is a multi-frequency source and the object/transmitter/receiver geometry is fixed. We show that a parametric reconstruction in this inverse problem is not possible; this is true for both the Born and Rytov inversions. However, the available reflected data can be used to accurately reconstruct the surface (shape) of objects. Reconstruction results for penetrable and non-penetrable objects are presented.KeywordsAttenuation CoefficientSpatial DomainTest ObjectSource FunctionScattered WaveThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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This paper presents a new method that combines a medial axis and implicit surfaces in order to reconstruct a 3D solid from an unstructured set of points scattered on the object's surface. The representation produced is based on iso‐surfaces generated by skeletons, and is a particularly compact way of defining a smooth free‐form solid. The method is based on the minimisation of an energy representing a “distance” between the set of data points and the iso‐surface, resembling previous reserach19. Initialisation, however, is more robust and efficient since there is computation of the medial axis of the set of points. Instead of subdividing existing skeletons in order to refine the object's surface, a new reconstruction algorithm progressively selects skeleton‐points from the pre‐ computed medial axis using an heuristic principle based on a “local energy” criterion. This drastically speeds up the reconstruction process. Moreover, using the medial axis allows reconstruction of objects with complex topology and geometry, like objects that have holes and branches or that are composed of several connected components. This process is fully automatic. The method has been successfully applied to both synthetic and real data.