2D 3D Research Articles

2D Ising critical couplings from quantum gravity

Using an exact holographic duality formula between the inhomogeneous 2D Ising model and 3D quantum gravity, we provide a formula for “real” zeros of the 2D Ising partition function on finite trivalent graphs in terms of the geometry of a 2D triangulation embedded in the three-dimensional Euclidean space. The complex phase of those zeros is given by the dihedral angles of the triangulation, which reflect its extrinsic curvature within the ambient 3D space, while the modulus is given by the angles within the 2D triangles, thus encoding the intrinsic geometry of the triangulation. Our formula cannot cover the whole set of Ising zeros, but we conjecture that a suitable complexification of these “real” zeros would provide a more thorough formula. Nevertheless, in the thermodynamic limit, in the case of flat planar 2D triangulations, our Ising zeros’ formula gives the critical couplings for isoradial graphs, confirming its generality. Finally, the formula naturally extends to graphs with arbitrary valence in terms of the geometry of circle patterns embedded in 3D space. This approach shows an intricate, but precise, new relation between statistical mechanics and quantum geometry. Published by the American Physical Society 2025

A New Efficient Hybrid Technique for Human Action Recognition Using 2D Conv-RBM and LSTM with Optimized Frame Selection

Recognizing human actions through video analysis has gained significant attention in applications like surveillance, sports analytics, and human–computer interaction. While deep learning models such as 3D convolutional neural networks (CNNs) and recurrent neural networks (RNNs) deliver promising results, they often struggle with computational inefficiencies and inadequate spatial–temporal feature extraction, hindering scalability to larger datasets or high-resolution videos. To address these limitations, we propose a novel model combining a two-dimensional convolutional restricted Boltzmann machine (2D Conv-RBM) with a long short-term memory (LSTM) network. The 2D Conv-RBM efficiently extracts spatial features such as edges, textures, and motion patterns while preserving spatial relationships and reducing parameters via weight sharing. These features are subsequently processed by the LSTM to capture temporal dependencies across frames, enabling effective recognition of both short- and long-term action patterns. Additionally, a smart frame selection mechanism minimizes frame redundancy, significantly lowering computational costs without compromising accuracy. Evaluation on the KTH, UCF Sports, and HMDB51 datasets demonstrated superior performance, achieving accuracies of 97.3%, 94.8%, and 81.5%, respectively. Compared to traditional approaches like 2D RBM and 3D CNN, our method offers notable improvements in both accuracy and computational efficiency, presenting a scalable solution for real-time applications in surveillance, video security, and sports analytics.

Hyperspectral Image Classification Based on Hybrid Depth-Wise Separable Convolution and Dual-Branch Feature Fusion Network

Recently, advancements in convolutional neural networks (CNNs) have significantly contributed to the advancement of hyperspectral image (HSI) classification. However, the problem of limited training samples is the primary obstacle to obtaining further improvements in HSI classification. The traditional methods relying solely on 2D-CNN for feature extraction underutilize the inter-band correlations of HSI, while the methods based on 3D-CNN alone for feature extraction lead to an increase in training parameters. To solve the above problems, we propose an HSI classification network based on hybrid depth-wise separable convolution and dual-branch feature fusion (HDCDF). The dual-branch structure is designed in HDCDF to extract simultaneously integrated spectral–spatial features and obtain complementary features via feature fusion. The proposed modules of 2D depth-wise separable convolution attention (2D-DCAttention) block and hybrid residual blocks are applied to the dual branch, respectively, further extracting more representative and comprehensive features. Instead of full 3D convolutions, HDCDF uses hybrid 2D–3D depth-wise separable convolutions, offering computational efficiency. Experiments are conducted on three benchmark HSI datasets: Indian Pines, University of Pavia, and Salinas Valley. The experimental results show that the proposed method showcases superior performance when the training samples are extremely limited, outpacing the state-of-the-art method by an average of 2.03% in the overall accuracy of three datasets, which shows that HDCDF has a certain potential in HSI classification.

A Robust Method for Real Time Intraoperative 2D and Preoperative 3D X-Ray Image Registration Based on an Enhanced Swin Transformer Framework

In image-guided surgery (IGS) practice, combining intraoperative 2D X-ray images with preoperative 3D X-ray images from computed tomography (CT) enables the rapid and accurate localization of lesions, which allows for a more minimally invasive and efficient surgery, and also reduces the risk of secondary injuries to nerves and vessels. Conventional optimization-based methods for 2D X-ray and 3D CT matching are limited in speed and precision due to non-convex optimization spaces and a constrained searching range. Recently, deep learning (DL) approaches have demonstrated remarkable proficiency in solving complex nonlinear 2D–3D registration. In this paper, a fast and robust DL-based registration method is proposed that takes an intraoperative 2D X-ray image as input, compares it with the preoperative 3D CT, and outputs their relative pose in x, y, z and pitch, yaw, roll. The method employs a dual-channel Swin transformer feature extractor equipped with attention mechanisms and feature pyramid to facilitate the correlation between features of the 2D X-ray and anatomical pose of CT. Tests on three different regions of interest acquired from open-source datasets show that our method can achieve high pose estimation accuracy (mean rotation and translation error of 0.142° and 0.362 mm, respectively) in a short time (0.02 s). Robustness tests indicate that our proposed method can maintain zero registration failures across varying levels of noise. This generalizable learning-based 2D (X-ray) and 3D (CT) registration algorithm owns promising applications in surgical navigation, targeted radiotherapy, and other clinical operations, with substantial potential for enhancing the accuracy and efficiency of image-guided surgery.

A novel approach to visual image encryption: 2D hyperchaos, variable Josephus, and 3D diffusion

Abstract With the development of the Internet, image encryption technology has become critical for network security. Traditional methods often suffer from issues such as insufficient chaos, low randomness in key generation, and poor encryption efficiency. To enhance performance, this paper proposes a new encryption algorithm designed to optimize parallel processing and adapt to images of varying sizes and colors. The method begins by using SHA-384 to extract the hash value of the plaintext image, which is then processed to determine the chaotic system’s initial value and block size. The image is padded and divided into blocks for further processing. A novel two-dimensional infinite collapses hyperchaotic map (2D-ICHM) is employed to generate the intra-block scrambling sequence, while n improved variable Joseph traversal sequence is used for inter-block scrambling. After removing the padding, 3D forward and backward shift diffusions, controlled by the 2D-ICHM sequences, are applied to the scrambled image, producing the ciphertext. Simulation results demonstrate that the proposed algorithm outperforms others in terms of entropy, anti-noise resilience, correlation coefficient, robustness, and encryption efficiency.

Noise-augmented chaotic Ising machines for combinatorial optimization and sampling

Ising machines are hardware accelerators for combinatorial optimization and probabilistic sampling, using stochasticity to explore spin configurations and avoid local minima. We refine the previously proposed coupled chaotic bits (c-bits), which operate deterministically, by introducing noise. This improves performance in combinatorial optimization, achieving algorithmic scaling comparable to probabilistic bits (p-bits). We show that c-bits follow the quantum Boltzmann law in a 1D transverse field Ising model. Furthermore, c-bits exhibit critical dynamics similar to p-bits in 2D Ising and 3D spin glass models. Finally, we propose a noise-augmented c-bit approach via the adaptive parallel tempering algorithm (APT), which outperforms fully deterministic c-bits running simulated annealing. Analog Ising machines with coupled oscillators could draw inspiration from our approach, as running replicas at constant temperature eliminates the need for global modulation of coupling strengths. Ultimately, mixing stochasticity with deterministic c-bits yields a powerful hybrid computing scheme that can offer benefits in asynchronous, massively parallel hardware implementations.

Automatic determination of 3D particle morphology from multiview images using uncertainty‐evaluated deep learning

AbstractParticle morphology is a crucial factor influencing the mechanical properties of granular materials particularly in infrastructure construction processes where accurate shape descriptors are essential. Accurately measuring three‐dimensional (3D) morphology has significant theoretical and practical value for exploring the multiscale mechanical properties of civil engineering materials. This study proposes a novel approach using multiview (two‐dimensional [2D]) particle images to efficiently predict 3D morphology, making real‐time aggregate quality analysis feasible. A 3D convolutional neural network (CNN) model is developed, which combines Monte Carlo dropout and attention mechanisms to achieve uncertainty‐evaluated predictions of 3D morphology. The model incorporates a convolutional block attention module, involving a two‐stage attention mechanism with channel attention and spatial attention, to further optimize feature representation and enhance the effectiveness of the attention mechanism. A new dataset comprising 18,000 images of 300 natural gravel and 300 blasted rock fragment particles is used for model training. The prediction accuracy and uncertainty of the proposed model are benchmarked against a range of alternative models including 2D CNN, 3D CNN, and 2D CNN with attention, in particular, to the influence of the number of input multiview particle images on the performance of the models for predicting various morphological parameters is explored. The results indicate that the proposed 3D CNN model with the attention mechanism achieves high prediction accuracy with an error of less than 10%. Whilst it exhibits initially greater uncertainty compared to other models due to its increased complexity, the model shows significant improvement in both accuracy and uncertainty as the number of training images is increased. Finally, residual challenges associated with the prediction of more complex particle angles and irregular shapes are also discussed.

Continuously focus tunable 2D/3D switchable cylindrical liquid crystal microlens arrays for autostereoscopic displays.

Cylindrical microlens arrays are important optical elements for autostereoscopic display. Conventional fixed focal length cylindrical microlens arrays do not allow switching between 2D mode and 3D mode when constructing a 3D viewing zone. In contrast, cylindrical liquid crystal microlens arrays with zoom characteristics allow switching between 2D and 3D states, as well as adjusting the width of the sub-viewing zone. Therefore, based on the quantitative analysis of the geometrical structure of the viewing zone in different states, this paper proposes a continuous zoom type cylindrical liquid crystal microlens array structure, which is a liquid crystal cell composed of an array of plano-concave glass substrates and planar glass substrates. Theory and experiments show that it is close to a favorable parabolic phase profile at different driving voltages, and at the same time, it can realize a zoom range of 1.6mm-36 mm at a smaller driving voltage. The wide zoom range and excellent zoom effect make this structure particularly suitable for autostereoscopic display, and this characteristic can achieve the effect of switching between 2D and 3D by adjusting the shape of the central viewing zone.

Prospective Evaluation of Accelerated Brain MRI Using Deep Learning-Based Reconstruction: Simultaneous Application to 2D Spin-Echo and 3D Gradient-Echo Sequences.

To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss' kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%-51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34-0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradient-echo sequences without compromising volumetry, including lesion quantification.

Soft template-assisted design and synthesis of anisotropic 2D–3D CuInS2 with a controlled morphology and band gap: exploring photothermal interfacial water evaporation

Synthesizing I–III–VI2 ternary semiconductor chalcogenide nanostructures with precise control over their shape and bandgap is a major focus of current research.

SiRNA Treatment Enhances Collagen Fiber Formation in Tissue-Engineered Meniscus via Transient Inhibition of Aggrecan Production.

The complex collagen network of the native meniscus and the gradient of the density and alignment of this network through the meniscal enthesis is essential for the proper mechanical function of these tissues. This architecture is difficult to recapitulate in tissue-engineered replacement strategies. Prenatally, the organization of the collagen fiber network is established and aggrecan content is minimal. In vitro, fibrochondrocytes (FCCs) produce proteoglycans and associated glycosaminoglycan (GAG) chains early in culture, which can inhibit collagen fiber formation during the maturation of tissue-engineered menisci. Thus, it would be beneficial to both specifically and temporarily block deposition of proteoglycans early in culture. In this study, we transiently inhibited aggrecan production by meniscal fibrochondrocytes using siRNA in collagen gel-based tissue-engineered constructs. We evaluated the effect of siRNA treatment on the formation of collagen fibrils and bulk and microscale tensile properties. Specific inhibition of aggrecan production by fibrochondrocytes via siRNA was successful both in 2D monolayer cell culture and 3D tissue culture. This inhibition during early maturation of these in vitro constructs increased collagen fibril diameter by more than 2-fold. This increase in fibril diameter allowed these tissues to distribute strains more effectively at the local level, particularly at the interface of the bone and soft tissue. These data show that siRNA can be used to modulate the ECM to improve collagen fiber formation and mechanical properties in tissue-engineered constructs, and that a transient decrease in aggrecan promotes the formation of a more robust fiber network.

A high-efficient electrochemical degradation of diclofenac in water on planar and microstructured 2D, and macroporous 3D boron-doped diamond electrodes: Identification of degradation and transformation products.

The highly efficient degradation of persistent organic substances by electrochemical advanced oxidation processes (EAOPs), which don't result in the formation of potentially harmful by-products, is crucial for the future of water management. In this study, boron-doped diamond electrodes (BDDE) with three morphologies (planar 2D, microstructured 2D, and macroporous 3D) were employed for the anodic oxidation of diclofenac (DCF) in two working electrolytes (NaCl and Na2SO4). In total, 11 by-products formed during the electrochemical oxidation of DCF were identified via HPLC-HRMS. The identification of degradation products revealed the formation of various active chlorinated species. The utilization of a chlorine-free Na2SO4 electrolyte resulted in the formation of greater number of chlorinated species, while their elimination required a longer period compared to the use of NaCl electrolyte. The formation of by-products was also influenced by the specific type of BDD electrode, which was associated with variations in applied current density. This led to an uneven distribution of dichloro (2D BDDE) and trichloro (3D BDDE) patterns. However, none of the products showed signs of a high level of persistence. The results revealed that the type of electrolyte is the most significant factor affecting the removal efficiency of DCF, while the different electrode morphologies do not lead to differences in the removal rates. The electrode type exerted a notable influence on the removal rates, which was associated with varying applied current densities, exclusively in the case of the Na₂SO₄ electrolyte. Over 99 % removal efficiency for DCF in NaCl, with power consumption of 1.8 kWh m-3 was achieved.

Macrovascular contributions to resting-state fMRI signals: A comparison between EPI and bSSFP at 9.4 Tesla

Abstract The draining-vein bias of T2*-weighted sequences, like gradient echo echo-planar imaging (GRE-EPI), can limit the spatial specificity of functional MRI (fMRI). The underlying extravascular signal changes increase with field strength (B0) and the perpendicularity of draining veins to the main axis of B0, and are therefore particularly problematic at ultra-high field (UHF). In contrast, simulations showed that T2-weighted sequences are less affected by the draining-vein bias, depending on the amount of rephasing of extravascular signal. As large pial veins on the cortical surface follow the cortical folding tightly, their orientation can be approximated by the cortical orientation to B→0. In our work, we compare the influence of the cortical orientation to B→0 on the resting-state fMRI signal of three sequences aiming to understand their macrovascular contribution. While 2D GRE-EPI and 3D GRE-EPI (both T2*-weighted) showed a high dependence on the cortical orientation to B→0, especially on the cortical surface, this was not the case for 3D balanced steady-state free precession (bSSFP) (T2/T1-weighted). Here, a slight increase of orientation dependence was shown in depths closest to WM. And while orientation dependence decreased with increased distance to the veins for both EPI sequences, no change in orientationdependence was observed in bSSFP. This indicates the low macrovascular contribution to the bSSFP signal, making it a promising sequence for layer fMRI at UHF.

Identification of Architectural Roman Remains in the Complex Archaeological Site of Buto ‘Tell El Fara'in’, Northern Egypt, Using Geophysical and Remote Sensing Data

ABSTRACTThe integrated use of remote sensing (RS) techniques, vertical magnetic gradient (VMG) and electrical resistivity tomography (ERT) measurements, and, in particular, combined analysis of 2D and 3D data, can provide a viable option for the identification of targets of interest at complicated archaeological sites. In this regard, a case study is Kom C at the archaeological site of Buto (Tell El Fara'in) in the northern Nile Delta (Egypt), where satellite data (Google Earth, Landsat 8 and OrbView‐3), VMG and ERT measurements were collected prior to site excavation. In this particular case, soil salinity in the buried structures, a lack of contrast in magnetic susceptibility and electrical resistivity, as well as the orientation, complex spatial distribution and overlapping of the architectural elements, all contributed to a number of anomalies that were difficult to interpret using only 2D results. Initially, the archaeological remains were identified as being made of mud‐brick based on land surface temperature (LST) estimated from thermal bands (Bands 10 and 11) in Landsat 8. Then, the high‐resolution satellite data, as well as the VMG and ERT (2D, quasi‐3D and full 3D resistivity models), were integrated to produce a comprehensive map of buried archaeological features. Excavations by Kafrelsheikh University in collaboration with the Ministry of Tourism and Antiquities recovered archaeological remains, including architectural elements that were perhaps used for official or administrative purposes or pottery‐making workshops during the Late Roman period (between the 4th and 7th century ce). The direct comparison of geophysical results to archaeological evidence from the excavation enabled a robust interpretation of geophysical anomalies visible in the horizontal resistivity depth slice and magnetic maps. As a whole, this case study highlights the value of combining satellite data with the analysis of 2D data and 3D views of geophysical surveys to better understand the real distribution of buried archaeological remains at similar complex sites.

Curcumin and Its Potential to Target the Glycolytic Behavior of Lactate-Acclimated Prostate Carcinoma Cells with Docetaxel.

Background: Dysregulated cellular metabolism is known to be associated with drug resistance in cancer treatment. Methods: In this study, we investigated the impact of cellular adaptation to lactic acidosis on intracellular energy metabolism and sensitivity to docetaxel in prostate carcinoma (PC) cells. The effects of curcumin and the role of hexokinase 2 (HK2) in this process were also examined. Results: PC-3AcT and DU145AcT cells that preadapted to lactic acid displayed increased growth behavior, increased dependence on glycolysis, and reduced sensitivity to docetaxel compared to parental PC-3 and DU145 cells. Molecular analyses revealed activation of the c-Raf/MEK/ERK pathway, upregulation of cyclin D1, cyclin B1, and p-cdc2Thr161, and increased levels and activities of key regulatory enzymes in glycolysis, including HK2, in lactate-acclimated cells. HK2 knockdown resulted in decreased cell growth and glycolytic activity, decreased levels of complexes I-V in the mitochondrial electron transport chain, loss of mitochondrial membrane potential, and depletion of intracellular ATP, ultimately leading to cell death. In a xenograft animal model, curcumin combined with docetaxel reduced tumor size and weight, induced downregulation of glycolytic enzymes, and stimulated the upregulation of apoptotic and necroptotic proteins. This was consistent with the in vitro results from 2D monolayer and 3D spheroid cultures, suggesting that the efficacy of curcumin is not affected by docetaxel. Conclusions: Overall, our findings suggest that metabolic plasticity through enhanced glycolysis observed in lactate-acclimated PC cells may be one of the underlying causes of docetaxel resistance, and targeting glycolysis by curcumin may provide potential for drug development that could improve treatment outcomes in PC patients.

A 3D calibration method for binocular vision system

Abstract Binocular vision system calibration is extremely important in three-dimensional (3D) measurement. During the calibration process, the establishment of an appropriate objective function has a direct impact on the calibration accuracy. For common two-dimensional (2D) calibration methods, differences in 2D calibration and 3D measurement coordinate systems result in loss of accuracy. Furthermore, for the newly proposed 3D calibration methods, the epipolar constraints in the 2D pixel coordinate system and the reconstruction errors in the 3D physical space need to be balanced by weights, resulting in uncertainties in the calibration results. To address these issues, we propose a novel calibration method that minimizes the distances between the control point and the left and right camera rays. The distances between the control points and the camera rays describe the 3D reconstruction errors, and the distances between the left camera and the right camera rays describe the geometry information between the two cameras to ensure that the epipolar constraints are satisfied. As a result, these two types of constraints/errors are measured under the same 3D coordinate system. Therefore, the epipolar constraints and the reconstruction errors are naturally balanced. We have conducted computer simulations and real experiments, and the results show that the proposed calibration method is effective in improving the accuracy compared with the state-of-the-art methods.

Boundary Knots Method with ghost points for high-order Helmholtz-type PDEs in multiply connected domains

This paper proposes the Boundary Knot Method with ghost points (BKM-G), which enhances the performance of the BKM for solving 2D (3D) high-order Helmholtz-type partial differential equations in domains with multiple cavities. The BKM-G differs from the conventional BKM by relocating the source points from the boundary collocation nodes to a random region, such as a circle (sphere) encompassing the original domain in 2D (3D). Compared with classical BKM, this modification improves accuracy without sacrificing simplicity and efficiency. Moreover, this paper investigates and analyzes the effect of the ghost circle/sphere’s radius R in BKM-G for solving various high-order Helmholtz-type PDEs. Numerous 2D and 3D numerical examples illustrate that the BKM-G outperforms the BKM for a wide range of R. The effectiveness of the proposed effective condition number (ECN) approach in finding the optimal R has also been demonstrated. Furthermore, the economic ECN (EECN) is studied to significantly improve the efficiency of ECN.

Assessment of peroneal tendon lesions using 2-dimensional and 3-dimensional isotropic magnetic resonance imaging with surgical correlation.

Accurate diagnoses of peroneal pathologies remains a challenge due to limitations of conventional 2D (dimensional) imaging, which can impact long-term patient outcomes. This study evaluates MRI accuracy and inter-reader reliability of peroneal compartment pathology for 2D and 3D MRI. A consecutive series of patients who underwent peroneal compartment surgery with preoperative 1.5- or 3.0-Tesla ankle MRIs from 2009 to 2024 included 32 scans (22 with 2D, 10 with 2D+3D) from 31 patients (ages 17-74 years, all genders). Three musculoskeletal readers blinded to surgical findings independently analyzed MRI scans for common peroneal tenosynovitis, peroneus brevis and peroneus longus tenosynovitis, tendinopathy, and tears. Inter-reader reliability and diagnostic performance measures were calculated. Using majority vote, overall accuracy, sensitivity, and specificity for peroneal tendons using 2D MRI were 80%, 81%, and 79%, respectively. Using 3D MRI sequences, whether in isolation or combination with 2D MRI, accuracy, sensitivity, and specificity increased to 85%, 88%, and 83%, respectively. The inter-reader reliability for peroneus brevis lesions was 0.45-0.75 for 2D, 0.25-0.35 for 3D, and 0.31-0.54 for combined 2D+3D, while for peroneus longus lesions, it was 0.45-0.90 for 2D, 0.20-0.71 for 3D, and 0.64-0.81 for combined 2D+3D scans. The inter-reader reliability for tenosynovitis ranged from 0.62-0.64 for 2D, 0.25-0.37 for 3D, and 0.57-0.66 for combined 2D+3D scans. The addition of 3D MRI to 2D high-resolution ankle MRI protocol or 3D MRI alone increases accuracy of peroneal compartment lesion detection with minor decrease in inter-reader reliability for peroneal brevis tendon assessment. Larger studies may help validate our findings.

Geoelectric Investigation for Andesite Reserves Using Dipole-Dipole Configuration in Batursari Subdistrict, Pekalongan Regency, Central Java, Indonesia

Batursari District is a region in Pekalongan, Central Java that is rich in andesite rock resources. Andesite rock is an igneous rock widely used in infrastructure construction, such as split casts and foundations. The geoelectric method is one technique that can be used to identify the presence of andesite rocks. This method uses a dipole-dipole configuration of a geoelectric survey. For this research, 12 lines were used in the northwest-southeast direction, each with a length of 280 meters and 10 m spacing. Res2DinV and Rockworks software produce 2D cross-sections and 3D models for inversion processing. The 3D model was analyzed to determine the distribution of andesite rocks in the research area. Based on the inversion processing, resistivity values were interpreted as follows: 0-150 Ωm as soil, 150-250 Ωm as weathered andesite, 250-400 Ωm as partial weathered andesite, and ≥ 400 Ωm as fresh andesite. The 2D inversion analysis and 3D modeling showed that the fresh andesite was more abundant in the south and decreased in the north direction. The total volume of andesite rock in the research area was calculated as it is 28.683.000 m³ with a density of 2.60 tons/m³. The total andesite rock reserve was calculated to be 52.630.200 tons with an indicated calculation of 70%, equating to 20.078.100 m³ and a tonnage of 36.841.000 tons/m3.

Back to geometry: Efficient indoor space segmentation from point clouds by 2D–3D geometry constrains

This paper addresses the challenge of indoor space segmentation from 3D point clouds, which is essential for understanding interior layouts, reconstructing 3D structures, and developing indoor navigation maps. While current deep learning-based methods rely on projecting 3D point clouds into 2D for instance extraction, they often fail to capture the local and global 3D features necessary for effectively segmenting complex indoor spaces, such as multi-ring nested structures. These methods also struggle with generalization across different scenes. In response, this paper proposes an efficient indoor space segmentation method that integrates both 2D and 3D geometric constraints. By leveraging the distribution characteristics of point clouds in 2D and the local and global features in 3D, the method achieves reliable extraction of vertical structural information in complex indoor environments. To address under-segmentation in small spaces due to varying scales, the paper introduces an adaptive extraction method for space partition anchors, guided by local features. During instance-level space segmentation, a hierarchical contour tree structure is employed to precisely partition complex indoor spaces, effectively handling circular and composite structures. The proposed approach was tested on 96 RGB-D scans from the Beike dataset and 6 large-scale indoor scenes from the S3DIS dataset, covering a range of complexities, sizes, and structures. The experimental section includes ablation studies and thorough comparisons with existing state-of-the-art spatial partitioning algorithms based on morphology and deep learning. Results demonstrate that the proposed method significantly outperforms existing approaches in terms of accuracy, robustness, and generalization ability, providing a solid foundation for indoor space modeling and robotic navigation. The source code and datasets will be made publicly available via the “EISPGeo” link.