Reconstruction Of Objects Research Articles

Computational temporal ghost imaging based on complementary modulation

Abstract We report an experimental demonstration of temporal ghost imaging in which a digital micromirror device (DMD) and +1/−1 binary modulation have been combined to give an accurate reconstruction of a nonperiodic time object. Compared to the 0/1 modulation, the reconstruction signal can be improved greatly by +1/−1 binary modulation even with half of the measurements. Experimental results show that 0/1 binary temporal objects up to 4 kHz and sinusoidal time objects up to 1 kHz can be reconstructed by this method. The influences of modulation speed and array detector gray levels are also discussed.

Reconstruction of a non-negative complex-valued object without a support constraint

Reconstruction of a non-negative complex-valued object without a support constraint

Criteria for objects suitable for reconstruction from holograms and diffraction patterns

In this study, quantitative criteria for reconstruction of objects from their hologram and diffraction patterns, and in particular for the phase objects in digital holography, are derived. The criteria that allow distinguishing the hologram and diffraction pattern are outlined. Gabor derived his criterion for objects suitable for holography based on the condition that the background in the reconstructed object’s distribution should be nearly flat so that its intensity contrast does not exceed 0.05. According to Gabor, an opaque object is suitable for holographic reconstruction if it occupies no more than 1% of the imaged area, and a phase-shifting object cannot be reconstructed in principle. We revisit these criteria and show that both amplitude-only and phase-only objects can be reconstructed when the object occupies less than 1% of the total illuminated area. In addition, a simplified derivation of the criteria is provided that is based on Parseval’s theorem. It is shown that for objects (including amplitude-only and phase-only) reconstructed from their holograms and the twin image treated as noise, a signal-to-noise ratio of 10 or higher can be achieved provided the object occupies less than 0.5% of the total illuminated area. When a hologram is reconstructed by applying iterative algorithms, the requirement for the object size is much more generous and identical to that applied in coherent diffraction imaging: any type of object (amplitude-only, phase-only, or amplitude-and-phase mixed properties) is suitable for holography when the object’s size in each dimension is less than half of the probed region’s extent (or the field of view).

AI upscaling Supporting Image Alignment in Photogrammetric Reconstruction

Abstract. This research aims to investigate and analyze the contribution provided by preventive image processing using AI in the SfM data acquisition process. Specifically, the objective is to observe qualities and defects of "AI upscaling" integrated into the normal workflow of digital restitution, with the hypothesis that greater sharpness and resolution can lead to better alignments and better model generations. Other similar experiments have been carried out previously on the AI intervention in the photos to improve the alignments, but a generic procedure and with untrained public AI has not yet been experimented. In the first phase of the research, the most suitable AI upscaling method for the purpose is selected, processing example images and comparing them with the original and various settings of the AI parameters to maintain likeness. In the second phase, the procedure efficiency was evaluated in several ways: through the automatic evaluation of Agisoft Metashape's Image Quality, by directly comparing the resulting model with the standard digital reconstruction of the same objects, comparing with a model produced with the native AI upscaling of the return program, and finally comparing with a high-level survey without defects. This test was repeated for four case studies specifically diversified by several factors, including subject scale, level of detail, lighting, color, blur, and type of camera used. Clear improvements emerge in all cases of AI upscaling, even reaching successful reconstructions that would not have started with the canonical method. It can therefore be stated that this preventive upscaling procedure is decisive in cases of low quality or damaged datasets, while in cases of already qualitatively sufficient photos it can be a useful support for increasing the level of detail.

Principles of stereo reconstruction of aerial objects using stationary cameras

ABSTRACT An overview is given here of the principles and mathematics of stereo reconstruction of objects in the sky using stationary cameras with an emphasis on meteorological applications. Through its Atmospheric Radiation Measurement program, the Department of Energy has operated stereo-photogrammetric cameras since 2017 as part of an effort to measure the life-cycle properties of clouds. At the core of that technology is stereo reconstruction, which calculates the real-world position of an object from the location of the object’s image in two cameras’ photographs. Here, stereo reconstruction is stripped down to its basic elements and presented using conventions tailored to applications in atmospheric science. In addition, the resulting equations are used to illustrate the high sensitivity of reconstructed cloud positions to errors in the cameras’ Euler angles. The interested reader will find here a self-contained guide to performing stereo reconstructions using distortion-corrected images from a pair of calibrated, stationary cameras, as well as a demonstration of the need for high accuracy in the measurement of camera properties and orientations.

Pose Tracking and Object Reconstruction Based on Occlusion Relationships in Complex Environments

Pose Tracking and Object Reconstruction Based on Occlusion Relationships in Complex Environments

PointNet + + Based Concealed Object Classification Utilizing an FMCW Millimeter-Wave Radar

AbstractIn the field of millimeter-wave (MMW) imaging, the integration of artificial intelligence (AI) has emerged as a crucial solution for addressing automation challenges. In this study, concealed object classification was successfully achieved on point cloud data from MMW radar high-precision imaging using the PointNet + + deep learning method. The utilized dataset comprises point cloud data generated through the transformation of 3D models and reconstruction of physical objects with an accuracy of less than 1 mm via MMW radar scanning. Classification accuracy was significantly improved by introducing data enhancement techniques, including the generation of homologous data and optimization of sampling points. After several evaluations, 300 epochs of training were conducted using 8192 sampling points, the results showed an accuracy of 0.998 for the training dataset and 0.996 for the test dataset. Moreover, evaluations of samples not included in the original dataset as well as multi-surface scans of concealed objects within the cardboard both resulted in correct predictions, which further validates the effectiveness and reliability of the study and demonstrates the potential of AI applied to MMW imaging.

High-resolution lensless holographic microscopy using a physics-aware deep network.

Lensless digital inline holographic microscopy (LDIHM) is an emerging quantitative phase imaging modality that uses advanced computational methods for phase retrieval from the interference pattern. The existing end-to-end deep networks require a large training dataset with sufficient diversity to achieve high-fidelity hologram reconstruction. To mitigate this data requirement problem, physics-aware deep networks integrate the physics of holography in the loss function to reconstruct complex objects without needing prior training. However, the data fidelity term measures the data consistency with a single low-resolution hologram without any external regularization, which results in a low performance on complex biological data. We aim to mitigate the challenges with trained and physics-aware untrained deep networks separately and combine the benefits of both methods for high-resolution phase recovery from a single low-resolution hologram in LDIHM. We propose a hybrid deep framework (HDPhysNet) using a plug-and-play method that blends the benefits of trained and untrained deep models for phase recovery in LDIHM. The high-resolution phase is generated by a pre-trained high-definition generative adversarial network (HDGAN) from a single low-resolution hologram. The generated phase is then plugged into the loss function of a physics-aware untrained deep network to regulate the complex object reconstruction process. Simulation results show that the SSIM of the proposed method is increased by 0.07 over the trained and 0.04 over the untrained deep networks. The average phase-SNR is elevated by 8.2dB over trained deep models and 9.8dB over untrained deep networks on the experimental biological cells (cervical cells and red blood cells). We showed improved performance of the HDPhysNet against the unknown perturbation in the imaging parameters such as the propagation distance, the wavelength of the illuminating source, and the imaging sample compared with the trained network (HDGAN). LDIHM, combined with HDPhysNet, is a portable and technology-driven microscopy best suited for point-of-care cytology applications.

A three-dimensional curve interface reconstruction algorithm for two-phase fluid flow

The curve interface reconstruction algorithm has received significant attention in the context of two-dimensional two-phase flow. However, it remains absent in the three-dimensional scenario. This paper proposes a novel three-dimensional curve interface reconstruction (CIR) algorithm to address this challenge within structured meshes for the first time. Specifically, a portion of the spherical surface is employed to reconstruct the three-dimensional curve interface segment, with the radius and center coordinates determined by curvature and mass conservation constraints, respectively. To enhance curvature accuracy, a sphere-based iterative reconstruction (SIR) algorithm is proposed to calculate the reconstructed distance function (RDF) for the three-dimensional curve interface. Various tests involving the interface reconstruction of spherical, ellipsoidal, and cubic objects demonstrate that the coupled SIR and CIR (SIR-CIR, simplified by SCIR) method achieves higher accuracy than many popular methods, particularly with coarse mesh resolutions. Additionally, the SCIR method offers the advantages of straightforward implementation and coding for interface reconstruction in two-phase flow research. This advantage results in reduced computational costs compared to the coupled volume-of-fluid and level set (VOSET) method, which also utilizes an iterative method to solve RDF.

Deep-learning based single-shot 3D reconstruction with simulated color-crosstalk and randomized extrinsics

In recent years, many single-frame 3D reconstruction schemes based on fringe projection profilometry (FPP) have been proposed. However, most single-frame reconstruction schemes still face the following three issues: (1) obtaining large datasets is very time-consuming, (2) focusing only on achieving single-frame reconstruction for white objects, and (3) requiring fixing the camera-projector positional relationship, increasing operational difficulty. By building a virtual FPP simulation system, our method can quickly render the required datasets, avoiding cumbersome manual operations. When rendering the datasets, we simulate adverse factors such as color channel crosstalk, system extrinsic parameter variations, and object surface colors to guide the training of the neural network. Ultimately, from a single three-frequency color image, the corresponding three-frequency three-step phase-shift images are predicted, achieving single-frame 3D reconstruction of colored objects and allowing some variation in system extrinsic parameters. Real-world experiments demonstrate that the network trained with the diverse data generated by our virtual system has good accuracy, providing valuable guidance for the practical application of deep learning methods.

Single‐Shot Non‐Invasive Imaging Through Dynamic Scattering Media Beyond the Memory Effect via Virtual Reference‐Based Correlation Holography

AbstractNon‐invasive wide‐field imaging through dynamic random media is a sought‐after goal with important applications ranging from medical diagnosis to remote sensing. However, some existing methods, such as speckle correlation‐based techniques, are limited in field of view due to the memory effect; while some other methods, such as wavefront shaping and transmission matrix techniques, face considerable challenges when applied in dynamic scenarios because of the complexity involved in modulation and measurement. These limitations significantly impede the effectiveness and applicability of these approaches. Here, the concept of virtual reference light (VRL), which allows for the reconstruction of the original object with just a single‐shot detection of the speckle is proposed. Experimental results demonstrate that the imaging field achieves a 3.8‐fold memory effect range. In the experimental setup, the light source and detector are positioned on one side of the random medium, while the sample is placed on the opposite side, enabling non‐invasive detection. Imaging results with both static and dynamic scattering media are presented to verify the feasibility of the proposed method, offering an effective solution for real‐time target imaging and detection.

High-precision integral imaging 3D salient object detection and reconstruction with texture features based on E2E-TransGAN

Under the framework of integral imaging, a high-precision 3D salient object detection and high-quality texture features reconstruction method is proposed by using the element to element-transformer and generative adversarial network (E2E-TransGAN). The proposed method fully exploits the global salient clues of the element image array to establish cross-regional element image dependency, thereby the accuracy of the salient prediction improved. Meanwhile, our proposed E2E-TransGAN algorithm stratifies the binary salient object area and the grayscale salient object edge, effectively addressing salient region prediction errors, obvious background noise, and color loss. The experimental results show that the detection accuracy of the proposed method improves to an average of 90% across various evaluation metrics. Compared with other methods, the proposed method can reconstruct 3D images with texture features, which have high values of PSNR and SSIM, at 30 and 0.95 on average, respectively. We have experimentally verified that the proposed method achieves precise 3D object salient detection and restores the detailed textures and depth information of 3D salient objects. The proposed method provides strong support for efficient and accurate medical image analysis and simplifies subsequent computer vision tasks.

Reconstruction of Maxillary Bone Defects With Cellular Bone Matrix Allografts

Study Design Retrospective Cohort Study. Objective Reconstruction of maxillary bone defects can be completed with vascularized and non-vascularized autografts. Cellular bone matrix allografts (CBMs), which have lineage committed bone cells, has risen as an alternative. The purpose of this study was to describe our experience and to determine the success of CBM based maxillary reconstruction in a variety of clinical scenarios. Methods A retrospective cohort study was designed and implemented using data from subjects who presented to the University of Louisville and were treated with a CBM for maxillary reconstruction from 2019 to 2023. Subjects were excluded if they were not treated with a CBM, data were not complete, or postoperative follow-up time was less than 3 months. Descriptive statistics were calculated for each variable. To measure the associations between the risk factors and graft success, Fisher’s exact test was implemented. A P-value of <0.05 was considered significant. Results The sample included 48 subjects. The mean age of all subjects was 43 ± 24 years. Overall, 42 (87.5%) cases were successful. The perioperative antibiotic administered ( P = 0.02), etiology ( P = 0.021), and the addition of platelet rich fibrin or autograft as an adjunct influenced CBM success ( P = 0.039). Conclusions CBMs are a viable option for reconstruction of maxillary bone defects. CBMs may be an alternative to autografts.

Second-order Spatial Measures Low Overlap Rate Point Cloud Registration Algorithm Based On FPFH Features1

When the sensor dynamically collects point cloud data for object or map reconstruction, the registration effect is poor and reconstruction application is difficult with a too low overlap rate of the collected point cloud data. The reason is that the objects are covered, the sensor rotation angle is too large and the speed of movement is too fast. Because of these problems, this paper proposes a point cloud registration algorithm based on FPFH feature matching, combined with second-order spatial measures. Firstly, using the FPFH feature extraction algorithm, the features of each point are extracted, and then feature matching is performed to generate the set of feature point pairs. Secondly, the second-order spatial measure is used to calculate the set of feature point pairs to obtain the second-order spatial measure matrix scores and sort them. Finally, the dichotomy method is used to find the appropriate second-order spatial measure scores for distinguishing the inner points (points in the overlap region) from the outer points (points that do not belong to the overlap region as well as the mismatched points and some disturbances). The contrast experiments between this algorithm and three common point cloud registration algorithms, FPFH-ICP, 4PCS-ICP, and NDT-ICP, on the Stanford dataset and 3DMatch dataset shows that the registration accuracy of the other algorithms decreases significantly with a low overlap rate. But this algorithm still has a high registration accuracy and is less affected by outliers than the other algorithms. Besides, this algorithm can still maintain a good registration effect on different data sets.

Three-dimensional object reconstruction based on spin–orbit interaction of light

Three-dimensional (3D) reconstruction has received extensive attention in recent years and is of great significance to various application fields. We propose a simple method of 3D object reconstruction based on spin–orbit interactions occurring from light reflection at the air–glass interface. By rotating the object, edge images of the object in various orientations are obtained; we overlap these edge images to ultimately reconstruct the 3D object. This 3D image detection based on spin–orbit interaction of light provides a low energy consumption, low cost, and more algorithmic method compared to traditional 3D image processing. This can be used in fields such as medicine, autonomous vehicles, planetary observation, and other areas.

Fg-ORKA: fast and gridless reconstruction of moving and deforming objects in multidimensional data

Abstract Identifying and tracking objects over multiple observations is a frequent task in many applications. Traffic monitoring requires the tracking of vehicles or pedestrians in video data and geophysical exploration relies on identifying seismic wave fronts from data of multiple sensors, only to mention two examples. In many cases, the object changes its shape or position within the given data from one observation to another. Vehicles can change their position and angle relative to the camera while seismic waves have different arrival times, frequencies, or intensities depending on the sensor position. This complicates the task at hand.

In a previous work, the authors presented a new algorithm to solve this problem - Object reconstruction using K-approximation (ORKA). This algorithm is hindered by two conflicting limitations: the tracked movement is limited by the sampling grid while the complexity increases exponentially with the resolution. We introduce an iterative variant of the ORKA algorithm that is able to overcome this conflict. We also give a brief introduction on the original ORKA algorithm. Knowledge of the previous work is thus not required.

We give theoretical error bounds and a complexity analysis which we validate with several numerical experiments. Moreover, we discuss the influence of different parameter choices in detail. The results clearly show that the iterative approach can outperform ORKA in both accuracy and efficiency. On the example of video processing we show that the new method can be applied where the original algorithm is too time and memory intensive. Furthermore, we demonstrate on seismic exploration data that we are now able to recover much finer details on the wave front movement then before.

The Adaption of Recent New Concepts in Neural Radiance Fields and Their Role for High-Fidelity Volume Reconstruction in Medical Images.

Volume reconstruction techniques are gaining increasing interest in medical domains due to their potential to learn complex 3D structural information from sparse 2D images. Recently, neural radiance fields (NeRF), which implicitly model continuous radiance fields based on multi-layer perceptrons to enable volume reconstruction of objects at arbitrary resolution, have gained traction in natural image volume reconstruction. However, the direct application of NeRF to medical volume reconstruction presents unique challenges due to differences in imaging principles, internal structure requirements, and boundary delineation. In this paper, we evaluate different NeRF techniques developed for natural images, including sampling strategies, feature encoding, and the use of complimentary features, by applying them to medical images. We evaluate three state-of-the-art NeRF techniques on four datasets of medical images of different complexity. Our goal is to identify the strengths, limitations, and future directions for integrating NeRF into the medical domain.

3D reconstruction and volume measurement of irregular objects based on RGB-D camera

Abstract To address the challenge of measuring volumes of irregular objects, this paper proposes a volume measurement method based on 3D point cloud reconstruction. The point clouds of the object with multiple angles are obtained from an RGB-D camera mounted on a robotic arm, and then are reconstructed to form a whole complete point cloud to calculate the volume of the object. Firstly, the robotic arm is controlled to move to four angles for capturing the original point clouds of the target. Then, by using the rotation and translation matrices obtained from the calibration block pre-registration, the point clouds data from the four angles are fused and reconstructed. Subsequently, the issue of missing bottom point cloud data is addressed using a bottom-filling algorithm. Following this, the efficiency of the point cloud volume calculation algorithm is enhanced through the application of AABB filtering. Finally, the reconstructed point cloud volume is calculated using a slicing algorithm that integrates 2D point cloud segmentation and point cloud sorting. Experimental results show that this method achieves a volume measurement accuracy of over 95% for irregular objects and exhibits good robustness.

A quantitative approach to reflectance transformation imaging in profilometric applications

Reflectance transformation imaging (RTI) is a technique employed to assess both the intensity and directional properties of light reflected from an object, aiming to visualize an object under different incident light directions. This technique has quickly become a commonly used approach for the documentation, acquisition and deciphering for cultural heritage objects, because it enables to enhance and highlight image details. The output of this mathematical synthesis technique, which elaborates illumination information, is contained in a pseudocolour image called normal map. Starting from this image, this technique allows to obtain the reconstruction of the outline of semi-flat objects by integrating the normal map given by the RTI, i.e. normal integration. The technique of normal integration has been known for many years, being widely used in graphical modelling across various fields of 3D imaging. In the context of cultural heritage, this particular form of 3D modelling has already found application in the reconstruction of semi-flat objects, albeit with some limitations in the accurate portraying of low spatial frequencies. However, quantitative and systematic studies on the efficiency of 3D rendering of objects using RTI and normal integration have not yet been conducted. In the context of this research, the quantitative evaluation of the accuracy of the RTI technique with normal integration for profilometric applications on semi-flat objects is proposed and a study on its fidelity in reproducing the considered artefact is conducted. For this purpose, ad hoc profilometric targets were designed, realized and analysed to quantitatively compare the results of RTI and normal integration with different standardized techniques: micro-photogrammetry, laser scanning and optical profilometry.Graphical abstract

Sensing electrical environments: mechanical object reconstruction via electrosensors

Abstract Increasing empirical evidence suggests that many terrestrial arthropods, such as bees, spiders, and caterpillars, sense electric fields in their environments. This relatively newly discovered sense may play a unique role within their broader sensory ecology, alongside other fundamental senses such as vision, hearing, olfaction, and aero-acoustic sensing. Deflectable hairs are the primary candidate for the reception of electrical stimuli. From the deflections of individually innervated hairs, the arthropod can transduce environmental and ecological information. However, it is unclear what information an animal can elicit from hair receptors and how it relates to their environment. This paper explores how an arthropod may ascertain geometric and electrical information about its environment. Using two-dimensional models, we explore the possibility of electroreceptive object recognition and reconstruction via multiple observations and several deflecting hairs. We analyse how the number of hairs, the observed shape, and the observation path alter the accuracy of the reconstructed representations. The results herein indicate the formidable possibility that geometric information about the environment can be electro-mechanically measured and acquired at a distance.