Three-dimensional Reconstruction Algorithm Research Articles

Natural scenes reveal diverse representations of 2D and 3D body pose in the human brain

Human pose, defined as the spatial relationships between body parts, carries instrumental information supporting the understanding of motion and action of a person. A substantial body of previous work has identified cortical areas responsive to images of bodies and different body parts. However, the neural basis underlying the visual perception of body part relationships has received less attention. To broaden our understanding of body perception, we analyzed high-resolution fMRI responses to a wide range of poses from over 4,000 complex natural scenes. Using ground-truth annotations and an application of three-dimensional (3D) pose reconstruction algorithms, we compared similarity patterns of cortical activity with similarity patterns built from human pose models with different levels of depth availability and viewpoint dependency. Targeting the challenge of explaining variance in complex natural image responses with interpretable models, we achieved statistically significant correlations between pose models and cortical activity patterns (though performance levels are substantially lower than the noise ceiling). We found that the 3D view-independent pose model, compared with two-dimensional models, better captures the activation from distinct cortical areas, including the right posterior superior temporal sulcus (pSTS). These areas, together with other pose-selective regions in the LOTC, form a broader, distributed cortical network with greater view-tolerance in more anterior patches. We interpret these findings in light of the computational complexity of natural body images, the wide range of visual tasks supported by pose structures, and possible shared principles for view-invariant processing between articulated objects and ordinary, rigid objects.

An effective point cloud registration method for three-dimensional reconstruction of pressure piping

Abstract At present, industrial scenes with sparse features and weak textures are widely encountered, and the three-dimensional reconstruction of such scenes is a recognized problem. Pressure pipelines have a wide range of applications in fields such as petroleum engineering, chemical engineering, and hydropower station engineering. However, there is no mature solution for the three-dimensional reconstruction of pressure pipes. The main reason is that the typical scenes in which pressure pipes are found also have relatively few features and textures. Traditional three-dimensional reconstruction algorithms based on feature extraction are largely ineffective for such scenes that are lacking in features. In view of the above problems, this paper proposes an improved interframe registration algorithm based on point cloud fitting with cylinder axis vector constraints. By incorporating geometric feature parameters of a cylindrical pressure pipeline, specifically the axis vector of the cylinder, to constrain the traditional iterative closest point algorithm, the accuracy of point cloud registration can be improved in scenarios lacking features and textures, and some environmental uncertainties can be overcome. Finally, using actual laser point cloud data collected from pressure pipelines, the proposed fitting-based point cloud registration algorithm with cylinder axis vector constraints is tested. The experimental results show that under the same conditions, compared with other open-source point cloud registration algorithms, the proposed method can achieve higher registration accuracy. Moreover, integrating this algorithm into an open-source three-dimensional reconstruction algorithm framework can lead to better reconstruction results.

Two-photon imaging of rabbit mammary tissue with 3D reconstruction

A broadband two-photon fluorescence (TPF) imaging system, based on a femtosecond oscillator, has been constructed. This system enables TPF imaging of rabbit mammary gland tissue samples by detecting TPF signals generated from the tissue upon excitation by broadband femtosecond pulses. Specifically, TPF imaging utilizes a detection window of 565–615 nm to capture TPF signals from the fluorescent adenine dinucleotide (FAD) present in lactating cells within breast tissue. Moreover, the 3D reconstruction algorithm has been optimized to visualize the 3D structure of the rabbit mammary gland acinar cavity.The optimized three-dimensional reconstruction algorithm can reduce ambiguous signal regions, making the reconstructed three-dimensional structure more reliable and accurate. This is of significant importance for studying the microscopic structure and cell distribution of breast tissue, contributing to a deeper understanding of the tissue's physiological status.

An Improved TransMVSNet Algorithm for Three-Dimensional Reconstruction in the Unmanned Aerial Vehicle Remote Sensing Domain.

It is important to achieve the 3D reconstruction of UAV remote sensing images in deep learning-based multi-view stereo (MVS) vision. The lack of obvious texture features and detailed edges in UAV remote sensing images leads to inaccurate feature point matching or depth estimation. To address this problem, this study improves the TransMVSNet algorithm in the field of 3D reconstruction by optimizing its feature extraction network and costumed body depth prediction network. The improvement is mainly achieved by extracting features with the Asymptotic Pyramidal Network (AFPN) and assigning weights to different levels of features through the ASFF module to increase the importance of key levels and also using the UNet structured network combined with an attention mechanism to predict the depth information, which also extracts the key area information. It aims to improve the performance and accuracy of the TransMVSNet algorithm's 3D reconstruction of UAV remote sensing images. In this work, we have performed comparative experiments and quantitative evaluation with other algorithms on the DTU dataset as well as on a large UAV remote sensing image dataset. After a large number of experimental studies, it is shown that our improved TransMVSNet algorithm has better performance and robustness, providing a valuable reference for research and application in the field of 3D reconstruction of UAV remote sensing images.

3-D ultrasonic imaging of bolt thread cracks using a linear array probe

The bolt is a widely-used standard component, and the cracking of its thread often leads to final fractures. Traditional 1-D (one-dimensional) A-scan detection and 2-D (two-dimensional) imaging methods only display the position and depth of bolt cracks, without providing information about the crack’s circumferential characteristics. While 1-D array linear scanning and 2-D array can achieve 3-D (three-dimensional) imaging in simple planar components, they have limitations when it comes to bolts. The former has a narrow scanning area, and the latter requires a long post-processing time. To overcome these limitations, the three-dimensional reconstruction algorithm (3-DR) and the half-matrix sparse total focus method (HMS-TFM) are proposed. Our approach involves adopting 360° circumferential scanning based on a 1-D linear array to achieve 3-D imaging of bolt cracks. At each scanning position, the 2-D image data are captured and are then fused to obtain the 3-D image. Different from the simple stacking of 2-D image data in linear scanning, our 3-DR algorithm reconstructs 3-D images based on discrete sampled 2-D images. To reduce the time required for 2-D imaging, the proposed HMS-TFM reduces the required data volume while maintaining acceptable image quality. Experiment results demonstrate that circumferential scanning based on the linear array can achieve 3-D imaging of bolt defects with lower time cost compared to using a matrix array for 3-D imaging. We obtained accurate circumferential quantification for radial-direction cracks with depths of 1 mm, 1.5 mm, and 2 mm with a relative error better than 6.23 %.

Deep investigation of muography in discovering geological structures in mineral exploration: a case study of Zaozigou gold mine

Summary Muography is a promising and rapidly developing physical prospecting technique based on the attenuation of muon flux. The feasibility and potential of applying muography to mining were presented in a small number of previous case studies. In this work, we aimed to address three challenges that limit the applicability and efficiency of muography in mineral exploration: (1) application to low-density-contrast ore body exploration, (2) analysis of altitudinal impacts on measurements, and (3) precise and efficient reconstruction. We conducted the first case of applying muography to the exploration for low-density-contrast ore bodies. Six muon imaging systems were placed underground to collect surviving muons for roughly half a year. We analyzed the altitudinal impact on the data measurements and proposed a simplified method to eliminate it. We also developed the seed algorithm, a novel three-dimensional reconstruction algorithm, that can reconstruct anomalies located far away from the detectors and avoid their elongation along the observed directions. Benefiting from the seed algorithm, a low-density-contrast orebody and a limonitic siliceous slate structure were reconstructed, demonstrating the sensitivity of this technique in density distinction; discoveries of several mined-out areas are important for accident avoidance; and reconstruction of the stope and scarps served as strong circumstantial evidence of the reliability of the result. The success of this experiment shows the great value of muography in the economic, research and safety aspects of mineral exploration and inspection. Moreover, the overcoming of challenges is helpful for the development of muography, making it an effective and competitive technique in this field.

The future of X-ray polarimetry towards the 3-dimensional photoelectron track reconstruction

The development of the first X-ray polarimeter, based on the photoelectric effect 20 years ago and implemented thanks to advances in gas amplification structures and readout techniques, had a significant impact in opening a new window for X-ray polarimetry. This system measures the X-ray polarization by reconstructing the initial direction of the photoelectron, emitted by the interaction of an incident photon with an atomic electron, in a gas mixture from an ionization track collected on a two-dimensional plane. However, actual X-ray polarimeters, are still requiring relatively long exposure time and cannot coupled with high effective area mirrors or concentrators. In this context, the high yield polarimetry experiment in X-rays (Hype-X) project is currently underway, aiming to improve the sensitivity of the next generation X-ray polarimetry detectors taking advantage of the recent advancements in imaging techniques for high-resolution time projection chambers. In particular, we are evaluating the use of TIMEPIX3 to be applied for the read-out of a gas detector, which will allow us to obtain a three-dimensional image of the photoelectron track. To evaluate the improvement achievable by using a 3D track reconstruction, in this paper, we have reproduced a three-dimensional photoelectron track from a `Geant4' Monte Carlo simulation and examined the sensitivity of X-ray polarimetry using a new three-dimensional track reconstruction algorithm. We report the improvement of the modulation factor with three-dimensional track reconstruction as ∼5% (relative) in the 2–8 keV range and ∼17% (relative) in the 2–4 keV range compared to the current two-dimensional polarimetry system. This is equivalent to add a further telescope to the three-telescope systems now employed in space on board the IXPE mission.

Muography as a support technique for non-invasive research and three-dimensional localization of tombs in archaeological sites: a case study from Palazzone Necropolis (Perugia – Italy)

Transmission muography is a non-invasive imaging technique that exploits the penetrating power of atmospheric muons into matter to obtain two-dimensional and three-dimensional density images of the monitored structure. The detectors used are particle trackers. Muography enables the monitoring of large structures and it is also particularly useful in the archaeological field for a mapping of low-density underground anomalies potentially related to unknown or inaccessible tombs or tunnels. The Palazzone necropolis, located south of Perugia (Italy), dating back to Etruscan period, contains about 200 known tombs, some of which, such as the Volumni Hypogeum, can be visited thanks to a touristic route. The eastern area of the necropolis, on the other hand, does not have a touristic path and is partially unknown. The objective of the muographic measurement campaign is to support the re-evaluation of this archaeological area by searching for new anthropic cavities and identifying them three-dimensionally. One of the goals of this study is to obtain a three-dimensional localization of cavities starting from a single muographic measurement by exploiting an image focusing algorithm. For this purpose, an area that contains a known cavity was used as the reference cavity for the test of the three-dimensional reconstruction algorithm.

A novel algorithm for three-dimensional shape reconstruction for microscopic objects based on shape from focus

A novel algorithm for three-dimensional shape reconstruction for microscopic objects based on shape from focus

Research on the Innovative Development of Digital Media Enabled by Computer Visual Arts

Abstract This paper explores the integration of computer visual art with digital media, highlighting the innovative potential of computer technology in creating dynamic digital media art. Initially, we review relevant theories of computer-based visual art and examine their applications in digital media art. We then detail an immersive scene design using Web3D technology, where images are processed via a computer vision camera imaging model, transformed, and analyzed with the SIFT algorithm to extract feature points. These are integrated with the incremental SFM algorithm for three-dimensional image reconstruction in immersive environments. We validate these methods with real datasets and controlled experiments to assess user experience improvements in digital media applications. Results indicate that the SIFT algorithm’s image-matching accuracy ranges from 50% to 78%, and the incremental SFM algorithm shows an average rotation error of only 0.183°. User sentiment scores improved significantly, from 11.583 to 23.452, post-experience, demonstrating enhanced realism and a new direction for digital media art.

A Non-Rigid Three-Dimensional Image Reconstruction Algorithm Based on Deformable Shape Reliability

A Non-Rigid Three-Dimensional Image Reconstruction Algorithm Based on Deformable Shape Reliability

Three-dimensional computer-generated holography based on the hybrid iterative angular spectrum algorithm.

This paper proposes a novel three-dimensional hologram calculation method based on the angular spectrum approach, with the aim of reducing the noise generated during the hologram reconstruction process. The proposed algorithm divides the spatial domain into multiple layers and employs the angular spectrum method to propagate the image between these layers, thus avoiding the paraxial approximation. To enhance the quality of the hologram, an error iteration algorithm is utilized to alleviate the occurrence of aliasing errors when directly superimposing holograms. Moreover, constraint factors are introduced between different layers within the same region to effectively utilize spatial resources for multi-image reconstruction, thereby mitigating the noise caused by inter-layer crosstalk. The feasibility of the proposed method is demonstrated through numerical simulations and optical experiments, highlighting its potential applicability to a wide range of three-dimensional reconstruction algorithms.

Algorithm for Reconstruction of Three-Dimensional Images in X-Ray Computed Tomography with a Cone Beam of Radiation

Purpose: Development of an algorithm for the reconstruction of three-dimensional images in X-ray
 computed tomography with a cone beam.
 Material and methods: A new approach to the development of the reconstruction algorithm is proposed.
 The algorithm is based on the exact analytical representation of the 3D Radon transform of the projection data. For such a representation, an iteration-invariant point spread function (PSF) is introduced.
 From the existing approximate algorithms of Feldkamp, Katsevich and other authors, the developed algorithm differs in increased accuracy, is applicable for high-performance spiral X-ray tomography, and
 provides a practical design of the X-ray optical path of the tomograph.
 Results: Mathematical modeling of the developed three-dimensional reconstruction algorithm for the
 helical trajectory of the radiation source was performed; satisfactory results were obtained on a phantom model simulating an object of study with contrast inserts. The influence of the distance of the radiation source from the center of coordinates with respect to the radius of the object of study is shown, for
 which a mathematical model of a special phantom in the form of a cube, consisting of separate uniform
 elliptical plates (ellipsoids) of the same density, was developed.
 The quality of the reconstructed tomographic images was assessed through by measurement of the difference between the image of the section of the phantom model and the reconstructed image, presented
 as the root mean square error.
 Conclusion: Studies have shown that for the ratio of the radius of the spiral trajectory to the radius of
 the object (R/Rm
 ), equal to about 3.0, i.e. at sufficiently large angles of the radiation cone of the source,
 the developed algorithm for three-dimensional reconstruction with a cone beam of radiation gives a tomographic image of high quality. This can be applied in the devel-opment of medical X-ray computed tomography.

Retracted: Corner Detection of Ceramic Crack Computer VR Image Based on Three-Dimensional Reconstruction Algorithm

Retracted: Corner Detection of Ceramic Crack Computer VR Image Based on Three-Dimensional Reconstruction Algorithm

Scan-free time-of-flight-based three-dimensional imaging through a scattering layer.

Reconstructing an object's three-dimensional shape behind a scattering layer with a single exposure is of great significance in real-life applications. However, due to the little information captured by a single exposure while strongly perturbed by the scattering layer and encoded by free-space propagation, existing methods cannot achieve scan-free three-dimensional reconstruction through the scattering layer in macroscopic scenarios using a short acquisition time of seconds. In this paper, we proposed a scan-free time-of-flight-based three-dimensional reconstruction method based on explicitly modeling and inverting the time-of-flight-based scattering light propagation in a non-confocal imaging system. The non-confocal time-of-flight-based scattering imaging model is developed to map the three-dimensional object shape information to the time-resolved measurements, by encoding the three-dimensional object shape into the free-space propagation result and then convolving with the scattering blur kernel derived from the diffusion equation. To solve the inverse problem, a three-dimensional shape reconstruction algorithm consisting of the deconvolution and diffractive wave propagation is developed to invert the effects caused by the scattering diffusion and the free-space propagation, which reshapes the temporal and spatial distribution of scattered signal photons and recovers the object shape information. Experiments on a real scattering imaging system are conducted to demonstrate the effectiveness of the proposed method. The single exposure used in the experiment only takes 3.5s, which is more than 200 times faster than confocal scanning methods. Experimental results show that the proposed method outperforms existing methods in terms of three-dimensional reconstruction accuracy and imaging limit subjectively and objectively. Even though the signal photons captured by a single exposure are too highly scattered and attenuated to present any valid information in time gating, the proposed method can reconstruct three-dimensional objects located behind the scattering layer of 9.6 transport mean free paths (TMFPs), corresponding to the round-trip scattering length of 19.2 TMFPs.

Pressure fluctuation and cellularization characteristics of 2-ethylfuran spherical expanding flame

The pressure fluctuation and cellularization characteristics of 2-ethylfuran (2-EF) liquid biofuel were investigated. The study was based on experiments in a constant volume combustion chamber (CVCC) under different initial temperatures (373, 403, and 433 K), initial pressures (1, 2, and 4 bar), and equivalence ratios (0.7–1.4). The spectrum analysis of the explosion pressure showed that the amplitude of pressure fluctuations was more intense with an increase in the equivalence ratio and initial pressure. Moreover, a three-dimensional reconstruction algorithm was constructed to analyze the evolution of cellular structures. It was found that both crack expansion and cell formation caused the irregular evolution of the flame front, which was more obvious at high equivalence ratios and initial pressures. Finally, the flame’s pressure fluctuation before reaching the quartz window was analyzed and combined with flame cellularization, which showed that the early pressure curve and cellularization parameters (cell number and crack length) had similar fluctuations, and the amplitude of the pressure fluctuation was influenced mainly by the initial pressure and the extent of flame cellularization.

A simple 3D reconstruction algorithm based on laser scanning: Research on flow characteristics of bubbles rising vertically in liquid phase

Bubbly flow is frequently observed in chemical industries such as reactors and steam boilers, where flow parameters of bubble velocity, size, and void fraction are required to guide process safety. In this paper, a laser scanning-based three-dimensional reconstruction algorithm is introduced to achieve simple and effective measurements in vertical bubbly flow. A series of slice images are captured by a high-speed camera, and a modified two-dimensional cross-correlation algorithm is proposed to achieve bubble extraction and matching. Based on the reconstructed flow structures of bubbly flow, bubble velocities and their distribution in different scanning cycles are measured. Statistically, bubble sizes are mainly concentrated within 1.8–2.2 mm. The void fractions are calculated for various gas flow rates with relative errors primarily within ±10%. The relative errors of the average void fraction are less than 8% by comparing the experimental and theoretical results, indicating a higher reconstruction precision.

Optimization RGB-D 3-D Reconstruction Algorithm Based on Dynamic SLAM

This paper proposes a robust dynamic feature segmentation SLAM three-dimensional (3D) reconstruction algorithm based on the dynamic segmentation framework of DynaSLAM in order to realise the Simultaneous Localization and Mapping (SLAM) system mapping in the dynamic environment of intelligent robots. The algorithm employs the method of feature point re-extraction to solve the problem of an insufficient number of original feature points. In the preprocessing stage, the optimized epipolar geometry model is designed in this paper, and the image is segmented jointly with Mask R-CNN. At the point cloud post-processing stage, kernel principal component analysis is used to reduce noise, and then this paper designs a dynamic filtering method based on octree for further processing. This processing effectively eliminates additional dynamic outliers. The results of experiments on TUM and Bonn data sets show that this algorithm achieves more than 10% better attitude estimation accuracy than the DynaSLAM algorithm in highly dynamic environment, and has advantages over the SOTA algorithms.

A 3D reconstruction method for rapid warehousing and acceptance of satellite batch production lines

The inspection of parameters such as payload flatness and interface accuracy of mass-produced satellite components is mainly completed manually by using special measuring tools. Because of its low inspection efficiency, insufficient batch inspection methods, and low degree of automation, it is impossible to continuously and stably complete high-quality inspections. Due to the shortcomings of repetitive and intelligent inspection work, a visual inspection system for fast warehousing and acceptance of satellite batch production lines based on machine vision is proposed. The multi-sensor camera is used for image acquisition, and the non-contact three-dimensional information acquisition is completed through the calculation of the three-dimensional reconstruction algorithm. The optical scanner based on multi-sensor fusion visual inspection integrates the robotic arm and the rotary table for automatic integration to realize a flexible automatic inspection scheme with seven degrees of freedom. The experimental results show that the detection system can accurately detect the size, flatness, and surface defects of satellite components in batches, and the detection accuracy can reach 0.01mm.

Abstract PR007: Three-dimensional genomic analysis of human pancreatic intraepithelial neoplasia (PanIN) reveals striking multifocality and genetic heterogeneity

Abstract Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease with a 5 year survival rate of 11%. PDAC arises from precursor lesions, the most common of which is pancreatic intraepithelial neoplasia (PanIN). Molecular analysis of human PanINs is critical to understand early pancreatic tumorigenesis, which could inform risk stratification, early detection, and cancer prevention approaches. Because PanINs are microscopic, it is challenging to determine their full extent and anatomic relationships using two dimensional histological sections. To determine the density and connectivity of PanINs, we utilized a novel machine learning algorithm (CODA) for three-dimensional (3D) reconstruction and cellular quantification. Large blocks of grossly normal pancreas were harvested from 39 surgical pancreatectomy specimens, followed by formalin fixation, paraffin-embedding, and complete serial sectioning. 3D reconstruction using CODA revealed striking multifocality of PanINs within the pancreata of most analyzed patients, with more than 600 spatially PanINs being modeled. Next, to determine whether these multifocal PanINs arose as independent neoplasms or via intraductal spread of a single PanIN, we assessed their clonal relationships using somatic multi-region DNA next generation sequencing (NGS). To do so, 8 additional blocks of grossly normal pancreatic tissue were harvested and 3D modeled, yielding 37 anatomically distinct PanINs for genomic analysis. Each spatially unconnected PanIN was separately microdissected in five different regions to assess both intra-PanIN and inter-PanIN genetic heterogeneity. 99 samples from the 37 PanIN lesions underwent targeted NGS using a panel covering major drivers of pancreatic ductal neoplasia. For PanINs with sufficient DNA, whole exome sequencing and deep sequencing of KRAS was also performed. Across the 8 blocks, 10 PanINs shared no mutations with other PanINs in the same block, indicating independent clonal origin. In addition, 8 PanINs shared only KRAS hotspot mutations with numerous other unshared mutations, likely indicating independent PanINs that shared hotspot mutations by chance. Six spatially separate low grade PanINs shared both driver and passenger mutations with at least one other spatially unconnected PanIN, demonstrating the ability of PanIN cells to dissociate from one lesion and establish unconnected, genetically related PanINs nearby. Furthermore, 5 PanINs harbored multiple KRAS mutations within a single PanIN, suggesting a polyclonal origin. One PanIN lacked any driver gene mutations. The genetic origins of the remaining 7 spatially separate PanINs could not be resolved due to a lack of discrete mutations found by targeted sequencing. Our 3D genomic analysis of anatomically distinct PanINs demonstrates that the unexpectedly large number of PanINs in normal pancreas most often arise independently, providing new foundations for our understanding of early pancreatic tumorigenesis. Determining the mechanisms for this multifocal neoplasia is an important direction for future research. Citation Format: Alicia M. Braxton, Ashley Kiemen, Rachel Karchin, Yuchen Jiao, Pei-Hsun Wu, Ralph H Hruban, Denis Wirtz, Laura D. Wood. Three-dimensional genomic analysis of human pancreatic intraepithelial neoplasia (PanIN) reveals striking multifocality and genetic heterogeneity [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr PR007.