3D Shape Research Articles

Beam Test of the First Prototype of SiPM-on-Tile Calorimeter Insert for the EIC Using 4 GeV Positrons at Jefferson Laboratory

We recently proposed a high-granularity calorimeter insert for the Electron-Ion Collider (EIC) that uses plastic scintillator tiles read out by SiPMs. Among its features are an ASIC-away-from-SiPM strategy for reducing cooling requirements and minimizing space use, along with employing 3D-printed frames to reduce optical crosstalk and dead areas. To evaluate these features, we built a 40-channel prototype and tested it using a 4 GeV positron beam at Jefferson Laboratory. The measured energy spectra and 3D shower shapes are well described by simulations, confirming the effectiveness of the design, construction techniques, and calibration strategy. This constitutes the first use of SiPM-on-tile technology in an EIC detector design.

Photocrosslinkable and hydroplasicable UV-shielding nanocellulose films facilitated by hydroxyl-yne click reaction

Sustainably-sourced functional nanocellulose materials are vitally important for the green and sustainable development. Herein, we reported photocrosslinkable and hydroplasticable TEMPO-oxidized cellulose nanofiber phenyl propylene ketone ethers (TOCNPPK) films with excellent ultraviolet (UV) shielding, highly reversible processability, and extended mechanical properties, which were facilitated by green hydroxyl-yne click reaction. The introduction of conjugated aromatic ring and vinyl bonds (-C=C-) had been demonstrated the key for the improved overall performance of resultant TOCNPPK, which not only endowed the TOCNPPK with nearly 100 % UV shielding, but also enabled it to be formed into diverse 3D shapes (helix, ring and letters “N, F, U”) via the facile hydrosetting method. The photocrosslinkable-enhanced mechanical performance of TOCNPPK films was also attributed to -C=C- which could crosslink via [2π + 2π] cycloaddition reactions under UV-irradiation. The ultimate stress of TOCNPPK films was as high as 210.0 ± 22.8 MPa and the Young's modulus was 11.5 ± 0.7 GPa, much superior to those of 128.6 ± 8.5 MPa and 9.2 ± 0.6 GPa for pristine TOCN films. Furthermore, the TOCNPPK had been demonstrated as efficient nanofillers for both hydrophilic polyvinyl alcohol and lipophilic polycaprolactone to develop advanced biodegradable composite films with the integration of good water-wetting resistance, excellent UV blocking, and photo-enhanced mechanical performance.

Real-time 3D shape measurement of dynamic scenes using fringe projection profilometry: lightweight NAS-optimized dual frequency deep learning approach.

Achieving real-time and high-accuracy 3D reconstruction of dynamic scenes is a fundamental challenge in many fields, including online monitoring, augmented reality, and so on. On one hand, traditional methods, such as Fourier transform profilometry (FTP) and phase-shifting profilometry (PSP), are struggling to balance measuring efficiency and accuracy. On the other hand, deep learning-based approaches, which offer the potential for improved accuracy, are hindered by large parameter amounts and complex structures less amenable to real-time requirements. To solve this problem, we proposed a network architecture search (NAS)-based method for real-time processing and 3D measurement of dynamic scenes with rate equivalent to single-shot. A NAS-optimized lightweight neural network was designed for efficient phase demodulation, while an improved dual-frequency strategy was employed coordinately for flexible absolute phase unwrapping. The experiment results demonstrate that our method can effectively perform 3D reconstruction with a reconstruction speed of 58fps, and realize high-accuracy measurement of dynamic scenes based on deep learning for what we believe to be the first time with the average RMS error of about 0.08 mm.

Walk in views: Multi-view path aggregation graph network for 3D shape analysis

The graph-based multi-view methods have achieved state-of-the-art results in 3D shape analysis tasks by taking advantage of graph convolutional networks (GCN) to process discrete data. However, the homogeneity of the traditional GCN aggregation operator leads to a problem in aggregating neighborhood information, i.e., if several views have the same neighbors, the same node embeddings will be generated, resulting in feature redundancy. To address this problem, we propose a Multi-view Path Aggregation Graph Network (MVPNet) for 3D shape analysis, which aims to extract a particular path from a graph composed of multiple views and aggregate it into an effective 3D shape descriptor. Specifically, we first extract a path in the graph through dynamic walking, and update the path status while searching for new nodes during the walking. Then we embed the position information of the nodes in the order of the nodes in the path. Finally, we propose to aggregate the features of a path employing a Path Transformer that is capable of handling ordered sequences. A path contains richer semantic and structural information than a traditional subgraph. To demonstrate the effectiveness of our proposed method, we conduct extensive experiments on three benchmark datasets, namely ModelNet, ShapeNetCore55 and MCB, and these experiments prove that the method outperforms the current methods in 3D shape classification and retrieval tasks.

Direct fabrication of glass microfluidic channel using CO2 laser

The design and development of small and compact devices with numerous new functions and vast benefits to the society is steadily receiving global attention. These sophisticated small devices require the production of accurate and intricate micro and nano-scale patterns with different three-dimensional (3D) shapes, sizes, and aspect ratios. Microfluidic device or so-called lab-on-a-chip (LOC) is one example of a highly sensitive and compact integrated device capable of detecting multiple analytes, usually used as a diagnostic device. In this study, we demonstrate the feasibility study for direct fabrication of microfluidic channel on transparent optical glass substrate via CO2 direct laser structuring. A custom-built direct laser structuring setup mainly consist of a commercial continuous type of CO2 laser source and glass preheating apparatus is utilized. First, the relationship between the process parameters, mainly the laser scanning speed, number of lasers passes and initial glass preheating temperature to the formation of microchannel of different width, height and shape were established. Then, the form accuracy and the morphology of the microchannels were characterized using laser scanning confocal microscope (LSCM) and surface profiler. Overall, the result reveals that the combination of higher laser power, lower scanning speed and higher number of laser passes promotes the increase of both the width and height of the microchannel. Preheating was found to be necessary for the glass used in this study in avoiding either micro-cracks or bulk glass cracking. Based on the relationship between laser scanning speed and the number of laser passes, a prototype of a microfluidic channel with average width and height of about 235 ± 10 µm and 6 ± 0.3 µm, respectively was successfully fabricated. This study opens an opportunity for further improvement and research for fabrication of crack-free and smooth microfluidic channels using low-cost CO2 laser structuring setup.

A newly revised estimation of bulk densities and examination of the shape of individual Ryugu grains

The bulk density of grains from a celestial body is a fundamental property related to its composition and structure, contributing to the understanding of its evolutionary history. In this study, we provide the bulk density of 637 grains returned from the C-type near-Earth asteroid 162173 Ryugu. This is the largest number of grains to date for the curation activity, corresponding to 38 wt.% of the total returned samples (approximately 5.4 g). Although several densities of the Ryugu grains were reported, the volume estimation of some samples showed uncertainties. Therefore, we applied a new volume estimation model calibrated by X-ray micro-computed tomography (XCT) to the Ryugu grains to more accurately estimate their bulk density. The obtained average bulk density of 637 Ryugu grains was 1.79 ± 0.31 g/cm3 (1σ variation) for weights of 0.5‒100 mg (sub-mm ‒to 10 mm) irrespective of their 3D shapes characterized by three axial length ratios, considered to be a representative of the returned samples. The bulk density distributions of the grains in Chambers A and C were statistically distinguishable, with mean values of 1.81 ± 0.30 and 1.76 ± 0.33 g/cm3 (1σ variations), respectively. Despite the small difference, bulk density may have differed by sampling site. The obtained average bulk density value of A + C samples was almost the same as that of 16 Ryugu grains estimated based on CT scanned data, and was consistent with the densities of CI chondrites (1.57–1.91 g/cm3). The axial ratios of the grains in Chambers A and C were similar and those of the 724 returned samples and the flying particles ejected during the sampling operations were also similar, suggesting that relatively small Ryugu materials (mm‒cm in size) are similar in shape. The minor difference between the Rygu grains and flying particles could be attributed to events such as scraping during sampling operations and transportation.Graphical

Multi‐Stimuli‐Responsive Weldable Bilayer Actuator with Programmable Patterns and 3D Shapes

AbstractSoft actuators can harvest environmental energy and convert it into kinetic energy for motions like bending, twisting, stretching, and contracting. However, it remains challenging to design soft film actuators for complex and programmable deformation in three dimensions. Herein, a weldable and patternable multi‐stimuli‐responsive bilayer soft actuator is developed by a mask‐assisted spraying coating process, and its 3D geometries are achieved by welding the sodium alginate (SA) layer using water. The intrinsic hygroscopicity of SA film and the magnetic and photothermal properties of Fe3O4 nanoparticles enable reversible deformation of the bilayer actuator under three different external stimuli: moisture, magnetic field, and sunlight. Based on these properties, a variety of multi‐stimuli‐responsive intelligent devices are developed including smart curtains, smart grippers, biomimetic walkers, rolling actuators, swimmers, and windmill rotators. All these actuating stimulations are derived from naturally renewable energy without the consumption of any artificial energy, providing important enlightenment for green and sustainable applications of soft actuators.

High-quality 3D shape recovery from scattering scenario via deep polarization neural networks

High-quality three-dimension(3D) shape recovery from scattering scenario is of great importance with various potential applications. However, in turbid water scenarios, the absorption and scattering by water and suspending particles have a great disturbance on the generation of image clarity, texture details, and pixel-wise polarized properties such as degree of polarization (DoP) and angle of polarization (AoP), which significantly affect the 3D reconstructed performance. In this study, we present a data-driven polarimetric method for 3D reconstruction of underwater objects in turbid scenes, leveraging deep learning image priors. An underwater descattering network is designed to fit the underwater transport function of target signal light and generate clear underwater images, which are utilized to enhance the texture and remove the backscattering light. Subsequently, accurate pixel-wise DoP and AoP of the object could be calculated using the clear image prior and underwater imaging model. Finally, utilizing the obtained clear image and polarimetric information, polarimetric 3D reconstruction of object in turbid underwater scenarios is achieved. Experimental results demonstrate that accurate 3D shape of objects in turbid water can be well-reconstructed. The proposed technique has great potential for advancing marine exploration research.

A Novel Prediction Model of the Drag Coefficient of Irregular Particles in Power-Law Fluids

The settlement drag coefficient of irregular particles in power-law fluids is a crucial parameter in the field of petroleum engineering. However, the irregular shape of the particle and the non-Newtonian rheological properties of the fluid make it challenging to predict the settlement drag coefficient. In this study, the spherical and irregular particle sedimentation processes in power-law fluids have been analyzed using a visual device and a high-speed camera system. A mechanical model dependent on the force balance of settlement particles was adopted to conduct a detailed statistical analysis of 114 spherical particle experimental results, and a prediction model of the drag coefficient of spherical particles in the power-law fluid was established with a mean relative error of 3.85%. On this basis, considering the influence of geometric shape on the law of particle sedimentation, a new irregular particle sedimentation resistance coefficient model in power-law fluid is established via the incorporation of the parameter circularity of 2D shape description c into the spherical particle sedimentation resistance coefficient predictive model. The parameters in the new irregular particle sedimentation resistance coefficient predictive model can be obtained via nonlinear data fitting of the 211 groups of irregular particles using experimental results in the power-law fluid. The model has high prediction accuracy for the drag coefficient of irregular particles in power-law fluid, with a mean relative error of 4.47, and expands the scope of engineering applications, which is of great significance for fracturing scheme design and wellbore cleaning.

NIMG-44. RADIOMIC FEATURES OF CONTRAST ENHANCED T1 MRI SEQUENCES PREDICT SURVIVAL IN PRIMARY GLIOBLASTOMA PATIENTS WHO UNDERWENT ADJUVANT RADIATION THERAPY

Abstract PURPOSE Glioblastoma (GBM) is an aggressive primary brain tumor that is monitored through magnetic resonance imaging (MRI). Radiomic analysis offers valuable insight into better understanding the imaging features of these tumors. We tested the hypothesis that changes in radiomic features following radiation treatment predicts overall survival. Radiomic features we were interested in included 2D and 3D shape features, first order features evaluating patterns of voxel intensity within the mask regions, and higher order GLCM features. METHODS Data from 19 patients from our brain bank diagnosed with a GBM at autopsy and underwent radiation therapy were used for this study. T1C tumor masks were created by manually annotating contrast enhancing tumor for patients on pre- and post-treatment MRIs. Pyradiomics was used to extract radiomic features within both pre- and post-radiation tumor masks, including 14 shape features, 18 first order features, and 75 higher order features were compared across timepoints and the difference for each variable was correlated with overall survival. RESULTS Of the tested radiomic features, only the following shape features were found to be correlated with overall survival: Major Axis Length (R = -0.55, p =.02), Maximum 2D Diameter Row (R= -.56, p =.02), Maximum 2D Diameter Slice (R= -.52, p =.03), and Maximum 3D Diameter (R= -.47, p = .04). CONCLUSIONS These results show a decrease in the Major Axis Length, Maximum 2D Diameter, Maximum 2D Diameter, and Maximum 3D diameter of the T1C masks post-radiation correlate with an overall increase in survival. These inverse trending radiomic variables may be useful to assess tumor response to radiation therapy, specifically through assessment of shape features within the contrast-enhancing tumor region. Future research should determine how tumor changes in response to other treatment modalities and/or correlates with other prognostic variables.

3D reconstruction of bone CT scan images based on deformable convex hull.

Three-dimensional (3D) reconstruction of computed tomography (CT) and magnetic resonance imaging (MRI) images is an important diagnostic method, which is helpful for doctors to clearly recognize the 3D shape of the lesion and make the surgical plan. In the study of medical image reconstruction, most researchers use surface rendering or volume rendering method to construct 3D models from image sequences. The watertightness of the algorithm-reconstructed surface will be affected by the segmentation precision or the thickness of the CT layer. The articular surfaces at femoral ends are often used in biomechanical simulation experiments. The model may not conform to its original shape due to the manual repair of non-watertight surfaces. To solve this problem, a 3D reconstruction method of leg bones based on deep learning is proposed in this paper. By deforming the convex hull of the target, comparing with state-of-the-art methods, our method can stably generate a watertight model with higher reconstruction accuracy. In the situation of target transition structures getting fuzzy and the layer spacing increasing, the proposed method can maintain better reconstruction performance and appear higher robustness. Also, the chamfer loss is optimized based on the rotational shape of the leg bones, and the weight of the loss function can be assigned according to the geometric characteristics of the target. Experiment results show that the optimization method improves the accuracy of the model. Furthermore, our research provides a reference for the application of deep learning in medical image reconstruction.

MDSSN: An end-to-end deep network on triangle mesh parameterization

Mesh data plays a crucial role in a wide range of applications worldwide within the field of 3D computer vision. In contrast to traditional Euclidean space arrangements, meshes encompass spatial information, including edges, faces, angles, and graph structures that embed face information within the mesh data. Nevertheless, conventional deep learning frameworks such as convolutional neural networks (CNNs) have struggled to achieve significant advancements in handling meshes. This paper proposes a simple mesh computation framework called Mesh Decomposition Second-order Sobolev Network (MDSSN) to model triangle meshes and represent their shape. The construction of face-based Riemannian and edge-based Riemannian graphs is motivated by the remarkable representation capabilities of edges and faces in mesh data, achieved through a graph composition mechanism. Furthermore, we design end-to-end operators, including the Sobolev-filter block, pooling, and unpooling blocks, which draw inspiration from traditional deep learning frameworks for modeling the mesh structure. Importantly, the design of the mesh-filter block incorporates the second-order Sobolev metric. To address common challenges in mesh classification and segmentation tasks, we have designed dedicated classification and segmentation modules. We utilize a joint loss function that integrates losses from adjacent faces and categories. We evaluate the performance of the MDSSN in mesh classification and segmentation tasks, focusing on its ability to represent 3D shapes. Experimental results demonstrate that MDSSN achieves superior performance compared to other state-of-the-art approaches.

2D Shape Detection for Solving Geometry Word Problems

The increasing scale of education has decreased the teacher–student ratio, making it difficult to keep track of student's progress individually. Therefore, teachers are leveraging artificial intelligence tools such as smart tutoring digital tools like learning management systems, and automated solvers for various subjects. Mathematics is one of the most fundamental subjects in school education. This work deals with facilitating the automatic solving of geometry math word problems (MWPs) at the elementary school level. To solve such problems, the information from the text as well as the accompanying figure needs to be interpreted. The preliminary task in solving such problems is to first identify which geometric shape is given in the question. Most existing systems or geometry solvers give the shape as one of the inputs to their systems to solve it further. In this work, the focus is on detecting the shape automatedly and proposing a technique for the same using traditional image processing methods. In addition, further, the triangles and quadrilaterals are classified into fine-grained classifications by taking into account their geometrical properties. Performance metrics like precision, recall, accuracy, and F1-score are used to present the results of this approach. The testing is done on various datasets that are readily available. The results show that on Geometry3K, GeoQA, and GeoS datasets the proposed technique attains an accuracy of 77.8%, 93.38%, and 95.02% and an F1 score of 60%, 71%, and 91.97% respectively. Like all other techniques, our system also has a limitation in that it does not detect all inscribed shapes.

Synthesis of novel 2D ZIF-8 single sheet from bulk multilayed ZIF-8 crystal, through facile mixed solutin strategy, with enhanced photocatalytic performance

Zeolitic Imidazole framework (ZIFs) have got an immense interest in many fields, due to its porous and functional structure with a tunable shape. The functional structure of ZIF-8 in 2D shape will combine both the characteristics of MOFs in 2D shape with unique properties. In this work, 2D thin Zeolitic imidazole framework (ZIF-8) has been prepared in a mixed solvent of water and methanol. The precursors of ZIF-8 in mixture solvent leads to a bulk multilayered ZIF-8 structure. Then the bulk layered ZIF-8 was delaminated into single sheets through physical sonication. The ZIF-8 sheets were investigated with different characterization techniques where, the hexagonal thin sheets with thickness around 200 to 250 nm in size, shows high stability with controlled morphological shape. ZIF-8 sheets show excellent performance for MB degradation and 94.05 % of dye has been removed as compared with 3D normal ZIF-8 crystals which only removed 67.7 % of MB dye. Furthermore, the optical and electrochemical performance shows that, the 2D ZIF-8 has better electrochemical values and confirm the effective separation of photo-generated charges, compared with ZIF-8 crystals.

Using 3D MRI Bone Shape to Predict Pre-Osteoarthritis of the Knee 2 Years After Anterior Cruciate Ligament Reconstruction

Background: Anterior cruciate ligament (ACL) injury increases risks for osteoarthritis (OA), a poorly modifiable and disabling condition. Joint changes of potentially reversible pre-OA have been described just 2 years after ACL reconstruction (ACLR) when early bone shape changes have also been reported. Purpose: This study evaluates relationships between interlimb differences in tibiofemoral bone shape derived from statistical shape modeling (SSM) of magnetic resonance imaging (MRI) and participant factors on patient-reported outcomes 2 years after unilateral ACLR. Study Design: Cross-sectional study; Level of evidence, 3. Methods: SSM-derived tibiofemoral bone shape and subchondral bone area were assessed from bilateral knee MRI scans of 72 participants with unilateral ACLR (mean age, 34 ± 11 years; 32 women) and compared with a reference cohort of 398 older individuals without OA (mean age, 50 ± 3 years; 213 women). Multivariable logistic regression models examined relationships between participant and surgical factors with interlimb differences in bone shapes or subchondral bone areas. Relationships between patient-reported outcomes and the interlimb differences in bone shape and subchondral area were examined using similar models. Results: Bone shape scores and subchondral bone areas were greater (more OA-like) in ACLR knees than uninjured contralateral knees in every bone metric tested (P≤ .001). Interlimb differences in femur shape scores of participants with ACLR were 65% greater (P < .001) than those of the significantly older reference cohort. Taller height, medial meniscal tears, and decreasing age were associated with larger interlimb differences in shape scores and subchondral areas (P < .05). Bone–patellar tendon–bone (BPTB) autograft recipients demonstrated greater interlimb subchondral area differences compared with allograft recipients (P < .05). Interlimb differences for hamstring autograft recipients did not differ from those with BPTB or allograft. Greater interlimb differences in medial femur subchondral areas were associated with worse patient-reported Knee injury and Osteoarthritis Outcome Score Symptoms (R = 0.27; P = .040). Conclusion: Even in the absence of radiographic OA, just 2 years after unilateral ACLR patients showed greater bone shape scores and subchondral areas consistent with pre-OA in their ACLR knees. Furthermore, greater medial femur bone areas were weakly associated with worse symptoms. Patients who are younger, are taller, have meniscal tears, or have BPTB grafts may be at increased risk for bony asymmetries 2 years after ACLR.

Predicting 3D particles shapes based on 2D images by using convolutional neural network

In order to accurately determine the three-dimensional (3D) shape of granular particles, one laborious approach is to employ X-ray microtomography and image processing techniques. As an alternative, in this study a 3D convolutional neural network (3D-CNN) methodology is presented to generate realistic 3D models of particles from a number of distinct two-dimensional (2D) projections. Initially, 3D synthetic particles with the targeted statistical distributions of the relevant geometric parameters, such as sphericity, elongation, aspect ratio, and flatness, were randomly generated using Fourier shape descriptors (FSDs). The database based on the statistical distributions of geometric parameters of Avicel® PH 200, obtained through dynamic image analysis. Following that, projections from multiple angles of 2D particle images were extracted for model training. By using a convolutional neural network (CNN) the shape of the particles was predicted, which involves the following sequential steps: a) model training on 2D images; b) a decoder is built to generate the 3D voxel model after an encoder extracts the properties of 2D images; c) a marching cube algorithm that converts the particle 3D voxel model into a mesh model; d) the accuracy of the prediction is evaluated by comparing the geometric parameters of the prediction models that were generated by using FSDs. The methodology was validated using a database of 3D synthetic particles with targeted statistical distributions of geometric parameters, such as sphericity, elongation, aspect ratio, and flatness. The mean absolute percentage error (MAPE) of the AI model for these parameters was 2.24%, 7.01%, 10.93%, and 7.67%, respectively. The 3D-CNN methodology has the potential for broad applications in various fields, such as rock and mineral analysis, battery materials, pharmaceuticals, and space exploration, offering a practical and affordable solution for shape analysis of relevant materials. It can significantly improve the efficiency and accuracy of numerical simulations and analyses, leading to new insights and innovations in a wide range of fields.

Abstract 11685: Effects of Pectus Excavatum Surgical Repair on Cardiac Volumes and Systolic Function as Assessed by 3D Transesophageal Echocardiography

Introduction: The consequences of external cardiovascular compression and cardiac displacement from pectus excavatum (PEx) are debated. The complex geometry of the right (RV) and left ventricle (LV) along with distortion from the extrinsic chest deformity pose significant limitations to reliable assessment of LV and RV volumes and function using transthoracic 2D-echocardiography in this population. Hypothesis: A 3D-echocardiography approach can reliably assess the effects of external cardiac compression and the benefits of surgical correction on RV and LV function in PEx patients. Methods: Adult patients undergoing minimally invasive repair of PEx between Oct-2021 and Feb-2023 were included in this prospective study. Intraoperative transesophageal (TEE) 6 beat full volume video loops incorporating LV and RV were captured and compared before and after surgical correction of the PEx. TomTec Software® was used for offline 3D/4D reconstruction ( Figure 1 shows changes in 3D RV and LV shape pre- and post-surgery). Result: The study included 102 patients (mean age of 32.1 ±11.8 years; Haller index 4.9 ±2.5; 60.8% males). Post PEx repair, there was a significant (p&lt;0.001) increase in the 3D RV volumes, RV ejection fraction (EF), RV stroke volume and RV strain ( Figure 1 ). Additionally, there was a significant improvement in 3D LVEF, 3D LV strain (longitudinal and circumferential), LV twist, and torsion ( Figure 1 ). Conclusion: In this PEx cohort, 3D/4D TEE imaging demonstrated significant increases in RV volumes and in RV and LV systolic function after surgical repair. These findings support the negative cardiovascular consequences of PEx compression and benefits of surgical repair.

Fully spherical 3D datasets on sedimentary particles: Fast measurement and evaluation

AbstractRecently it became increasingly evident that the statistical distributions of size and shape descriptors of sedimentary particles reveal crucial information on their evolution and may even carry the fingerprints of their provenance as fragments. However, to unlock this trove of information, measurement of traditional geophysical shape descriptors (mostly detectable on 2D projections) is not sufficient; fully spherical 3D imaging and mathematical algorithms suitable to extract new types of inherently 3D shape descriptors are necessary. Available 3D imaging technologies force users to choose either speed or full sphericity. Only partial morphological information can be extracted in the absence of the latter (e.g., LIDAR imaging). In the case of fully spherical imaging, speed was proved to be prohibitive for obtaining meaningful statistical samples, and inherently 3D shape descriptors were not extracted. Here we present a new method by complementing a commercial, portable 3D scanner with simple hardware to quickly obtain fully spherical 3D datasets from large collections of sedimentary particles. We also present software for the automated extraction of 3D shapes and automated measurement of inherently 3D-shape properties. This technique allows for examining large samples without the need for transportation or storage of the samples, and it may also facilitate the collaboration of geographically distant research groups. We validated our software on a large sample of pebbles by comparing previously hand-measured parameters with the results of automated shape analysis. We also tested our hardware and software tools on a large pebble sample in Kawakawa Bay, New Zealand.

Simplification of 2D shapes with equivalent rectangles

Simplification of 2D shapes with equivalent rectangles

PVFAN: Point-view fusion attention network for 3D shape recognition

3D shape recognition is a critical research topic in the field of computer vision, attracting substantial attention. Existing approaches mainly focus on extracting distinctive 3D shape features; however, they often neglect the model’s robustness and lack refinement in deep features. To address these limitations, we propose the point-view fusion attention network that aims to extract a concise, informative, and robust 3D shape descriptor. Initially, our approach combines multi-view features with point cloud features to obtain accurate and distinguishable fusion features. To effectively handle these fusion features, we design a dual-attention convolutional network which consists of a channel attention module and a spatial attention module. This dual-attention mechanism greatly enhances the generalization ability and robustness of 3D recognition models. Notably, we introduce a strip-pooling layer in the channel attention module to refine the features, resulting in improved fusion features that are more compact. Finally, a classification process is performed on the refined features to assign appropriate 3D shape labels. Our extensive experiments on the ModelNet10 and ModelNet40 datasets for 3D shape recognition and retrieval demonstrate the remarkable accuracy and robustness of the proposed method.