The generation of high-quality 3D models from single 2D images remains challenging in terms of accuracy and completeness.Deep learning has emerged as a promising solution,offering new avenues for improvements.However,...The generation of high-quality 3D models from single 2D images remains challenging in terms of accuracy and completeness.Deep learning has emerged as a promising solution,offering new avenues for improvements.However,building models from scratch is computationally expensive and requires large datasets.This paper presents a transfer-learning-based approach for category-specific 3D reconstruction from a single 2D image.The core idea is to fine-tune a pre-trained model on specific object categories using new,unseen data,resulting in specialized versions of the model that are better adapted to reconstruct particular objects.The proposed approach utilizes a three-phase pipeline comprising image acquisition,3D reconstruction,and refinement.After ensuring the quality of the input image,a ResNet50 model is used for object recognition,directing the image to the corresponding category-specific model to generate a voxel-based representation.The voxel-based 3D model is then refined by transforming it into a detailed triangular mesh representation using the Marching Cubes algorithm and Laplacian smoothing.An experimental study,using the Pix2Vox model and the Pascal3D dataset,has been conducted to evaluate and validate the effectiveness of the proposed approach.Results demonstrate that category-specific fine-tuning of Pix2Vox significantly outperforms both the original model and the general model fine-tuned for all object categories,with substantial gains in Intersection over Union(IoU)scores.Visual assessments confirm improvements in geometric detail and surface realism.These findings indicate that combining transfer learning with category-specific fine tuning and refinement strategy of our approach leads to better-quality 3D model generation.展开更多
Fracability is a critical indicator for evaluating the exploration and development potential of coalbed methane reservoirs and assessing the effectiveness of hydraulic fracturing stimulation operations.Its core functi...Fracability is a critical indicator for evaluating the exploration and development potential of coalbed methane reservoirs and assessing the effectiveness of hydraulic fracturing stimulation operations.Its core function is to characterize the complexity of the induced fracture network and the resulting effective stimulated volume.In this study,we quantified fracture area and geometric complexity using true triaxial fracturing experiments and computed tomography three-dimensional(3D)reconstruction technology,combined with the box-counting method to calculate the 3D fractal dimension of the fracture surfaces.The results revealed that the total fracture surface area per unit volume of the stimulated reservoir effectively characterized reservoir fracability;specifically,both a larger total fracture surface area and a higher fractal dimension corresponded to better reservoir fracability.Fracture complexity was enhanced by a decrease in the horizontal principal stress difference or an increase in the injection rate.Under optimal conditions of a 3 MPa stress difference and an injection rate of 60 mL/min,fracability improved by 27.6%.Furthermore,liquid carbon dioxide(CO_(2))improved fracability by 50.7%compared to using water as the fracturing fluid,a result attributed to its low viscosity and strong diffusion capacity,which activated a greater number of natural fractures.A fracability evaluation model integrating brittleness,fracture toughness,and dimensionless net pressure was developed using regression analysis,which demonstrated high reliability with a strong determination coefficient(R^(2))of 0.9019.This study clarifies the logical relationships among fracture area,complexity,and fractal dimension,providing a novel method for evaluating the fracability of coal reservoirs.展开更多
Optical coherence tomography(OCT),particularly Swept-Source OCT,is widely employed in medical diagnostics and industrial inspections owing to its high-resolution imaging capabilities.However,Swept-Source OCT 3D imagin...Optical coherence tomography(OCT),particularly Swept-Source OCT,is widely employed in medical diagnostics and industrial inspections owing to its high-resolution imaging capabilities.However,Swept-Source OCT 3D imaging often suffers from stripe artifacts caused by unstable light sources,system noise,and environmental interference,posing challenges to real-time processing of large-scale datasets.To address this issue,this study introduces a real-time reconstruction system that integrates stripe-artifact suppression and parallel computing using a graphics processing unit.This approach employs a frequency-domain filtering algorithm with adaptive anti-suppression parameters,dynamically adjusted through an image quality evaluation function and optimized using a convolutional neural network for complex frequency-domain feature learning.Additionally,a graphics processing unit integrated 3D reconstruction framework is developed,enhancing data processing throughput and real-time performance via a dual-queue decoupling mechanism.Experimental results demonstrate significant improvements in structural similarity(0.92),peak signal-to-noise ratio(31.62 dB),and stripe suppression ratio(15.73 dB)compared with existing methods.On the RTX 4090 platform,the proposed system achieved an end-to-end delay of 94.36 milliseconds,a frame rate of 10.3 frames per second,and a throughput of 121.5 million voxels per second,effectively suppressing artifacts while preserving image details and enhancing real-time 3D reconstruction performance.展开更多
As large,room-scale environments become increasingly common,their spatial complexity increases due to variable,unstructured elements.Consequently,demand for room-scale service robots is surging,yet most technologies r...As large,room-scale environments become increasingly common,their spatial complexity increases due to variable,unstructured elements.Consequently,demand for room-scale service robots is surging,yet most technologies remain corridor-centric,and autonomous navigation in expansive rooms becomes unstable even around static obstacles.Existing approaches face several structural limitations.These include the labor-intensive requirement for large-scale object annotation and continual retraining,as well as the vulnerability of vanishing point or linebased methods when geometric cues are insufficient.In addition,the high cost of LiDAR and 3D perception errors caused by limited wall cues and dense interior clutter further limit their effectiveness.To address these challenges,we propose a zero-shot vision-based algorithm for robust 3D map reconstruction in geometry-deficient room-scale environments.The algorithm operates in three layers:Layer 1 performs dimension-wise boundary detection;Layer 2 estimates vanishing points,refines the precise perspective space,and extracts a floor mask;and Layer 3 conducts 3D spatial mapping and obstacle recognition.The proposed method was experimentally validated across various geometric-deficient room-scale environments,including lobbies,seminar rooms,conference rooms,cafeterias,and museums—demonstrating its ability to reliably reconstruct 3D maps and accurately recognize obstacles.Experimental results show that the proposed algorithm achieved an F1 score of 0.959 in precision perspective space detection and 0.965 in floor mask extraction.For obstacle recognition and classification,it obtained F1 scores of 0.980 in obstacle absent areas,0.913 in solid obstacle environments,and 0.939 in skeleton-type sparse obstacle environments,confirming its high precision and reliability in geometric-deficient room-scale environments.展开更多
Spectrum map construction,which is crucial in cognitive radio(CR)system,visualizes the invisible space of the electromagnetic spectrum for spectrum-resource management and allocation.Traditional reconstruction methods...Spectrum map construction,which is crucial in cognitive radio(CR)system,visualizes the invisible space of the electromagnetic spectrum for spectrum-resource management and allocation.Traditional reconstruction methods are generally for twodimensional(2D)spectrum map and driven by abundant sampling data.In this paper,we propose a data-model-knowledge-driven reconstruction scheme to construct the three-dimensional(3D)spectrum map under multi-radiation source scenarios.We firstly design a maximum and minimum path loss difference(MMPLD)clustering algorithm to detect the number of radiation sources in a 3D space.Then,we develop a joint location-power estimation method based on the heuristic population evolutionary optimization algorithm.Considering the variation of electromagnetic environment,we self-learn the path loss(PL)model based on the sampling data.Finally,the 3D spectrum is reconstructed according to the self-learned PL model and the extracted knowledge of radiation sources.Simulations show that the proposed 3D spectrum map reconstruction scheme not only has splendid adaptability to the environment,but also achieves high spectrum construction accuracy even when the sampling rate is very low.展开更多
[Significance]In alignment with the national germplasm security strategy,current research efforts are accelerating the adoption of precision breeding in sheep.Within the whole-genome selection,accurate phenotyping of ...[Significance]In alignment with the national germplasm security strategy,current research efforts are accelerating the adoption of precision breeding in sheep.Within the whole-genome selection,accurate phenotyping of body morphometrics is critical for assessing growth performance and breeding value.Traditional manual measurements are inefficient,prone to human error,and may cause stress to sheep,limiting their suitability for precision sheep management.By summarizing the applications of sheep body size measurement technologies and analyzing their development directions,this paper provides theoretical references and practical guidance for the research and application of non contact sheep body size measurement.[Progress]This review synthesizes progress across three principal methodological paradigms:two-dimensional(2D)image-based techniques,three-dimensional(3D)point cloud-based approaches,and integrated 2D-3D fusion systems.2D methods,employing either handcrafted geometric features or deep learning-based keypoint detector algorithms,are cost-effective and operationally simple but sensitive to variation in imaging conditions and unable to capture critical circumference metrics.3D point-cloud approaches enable precise reconstruction of full animal morphology,supporting comprehensive body-size acquisition with higher accuracy,yet face challenges including high hardware costs,complex data workflows,and sensitivity to posture variability.Hybrid 2D-3D fusion systems combine semantic richness from RGB imagery with geometric completeness from point clouds.Having been effectively validated in other livestock specise,e.g.,cattle and pigs,these fusion systems have demonstrated excellent performance,providing important technical references and practical insights for sheep body size measurement.[Conclusions and Prospects]Firstly,future research should focus on constructing large-scale,high-quality datasets for sheep body size measurement that encompass diverse breeds,growth stages,and environmental conditions,thereby enhancing model robustness and generalization.Secondly,the development of lightweight artificial intelligence models is essential.Techniques such as model compression,quantization,and algorithmic optimization can substantially reduce computational complexity and storage requirements,facilitating deployment in resource-constrained environments.Thirdly,the 3D point cloud processing pipeline should be streamlined to improve the efficiency of data acquisition,filtering,registration,and segmentation,while promoting the integration of low-cost,high-resilience vision systems into practical farming scenarios.Fourthly,specific emphasis should be placed on improving the accuracy of curved-dimensional measurements,such as chest circumference,abdominal circumference,and shank circumference,through advances in pose standardization,refined 3D segmentation strategies,and multimodal data fusion.Finally,the cross-fertilization of sheep body size measurement technologies with analogous methods for other livestock species offers a promising pathway for mutual learning and collaborative innovation,accelerating the industrialization of automated sheep morphometric systems and supporting the development of intelligent,data-driven pasture management practices.展开更多
目的:探究3D全腹腔镜根治术治疗Ⅲ期远端胃癌的疗效及安全性。方法:收集2021年2月至2024年2月收治的Ⅲ期远端胃癌患者的临床资料。将接受腹腔镜辅助远端胃癌根治术的患者纳入对照组,将接受3D全腹腔镜辅助远端胃癌根治术的患者纳入观察组...目的:探究3D全腹腔镜根治术治疗Ⅲ期远端胃癌的疗效及安全性。方法:收集2021年2月至2024年2月收治的Ⅲ期远端胃癌患者的临床资料。将接受腹腔镜辅助远端胃癌根治术的患者纳入对照组,将接受3D全腹腔镜辅助远端胃癌根治术的患者纳入观察组,采用倾向性评分匹配法以最邻近匹配原则1∶1进行匹配,最终两组各纳入60例。比较两组手术情况、术后恢复情况、术后疼痛程度评分(VAS)、围手术期应激反应指标、胃肠功能指标及并发症发生情况。结果:与对照组相比,观察组切口长度、消化道重建时间、术后首次排气时间、自主下床活动时间及住院时间更短,使用镇痛药物患者比例更低,切口美容评分更高,术后并发症发生率更低(P<0.05)。术后8、24、48、72 h两组VAS均低于治疗前,且观察组低于对照组(P<0.05)。观察组术后24、72 h ACTH、PGE2升高幅度和MTL、GAS降低幅度均小于对照组(P<0.05)。结论:3D全腹腔镜根治术治疗Ⅲ期远端胃癌创伤小、术后并发症少,有利于胃肠功能早期恢复,且具有切口美观的优势。展开更多
基金funded by the Research,Development,and Innovation Authority(RDIA)—Kingdom of Saudi Arabia—under supervision Energy,Industry,and Advanced Technologies Research Center,Taibah University,Madinah,Saudi Arabia with grant number(12979-iau-2023-TAU-R-3-1-EI-).
文摘The generation of high-quality 3D models from single 2D images remains challenging in terms of accuracy and completeness.Deep learning has emerged as a promising solution,offering new avenues for improvements.However,building models from scratch is computationally expensive and requires large datasets.This paper presents a transfer-learning-based approach for category-specific 3D reconstruction from a single 2D image.The core idea is to fine-tune a pre-trained model on specific object categories using new,unseen data,resulting in specialized versions of the model that are better adapted to reconstruct particular objects.The proposed approach utilizes a three-phase pipeline comprising image acquisition,3D reconstruction,and refinement.After ensuring the quality of the input image,a ResNet50 model is used for object recognition,directing the image to the corresponding category-specific model to generate a voxel-based representation.The voxel-based 3D model is then refined by transforming it into a detailed triangular mesh representation using the Marching Cubes algorithm and Laplacian smoothing.An experimental study,using the Pix2Vox model and the Pascal3D dataset,has been conducted to evaluate and validate the effectiveness of the proposed approach.Results demonstrate that category-specific fine-tuning of Pix2Vox significantly outperforms both the original model and the general model fine-tuned for all object categories,with substantial gains in Intersection over Union(IoU)scores.Visual assessments confirm improvements in geometric detail and surface realism.These findings indicate that combining transfer learning with category-specific fine tuning and refinement strategy of our approach leads to better-quality 3D model generation.
基金supported by the Natural Science Foundation of China(Grant No.52574047 and Grant No.52374045)Key Project of Sichuan Provincial Joint Fund for Science Technology and Education,China(Grant No.2025NSFSC2008).
文摘Fracability is a critical indicator for evaluating the exploration and development potential of coalbed methane reservoirs and assessing the effectiveness of hydraulic fracturing stimulation operations.Its core function is to characterize the complexity of the induced fracture network and the resulting effective stimulated volume.In this study,we quantified fracture area and geometric complexity using true triaxial fracturing experiments and computed tomography three-dimensional(3D)reconstruction technology,combined with the box-counting method to calculate the 3D fractal dimension of the fracture surfaces.The results revealed that the total fracture surface area per unit volume of the stimulated reservoir effectively characterized reservoir fracability;specifically,both a larger total fracture surface area and a higher fractal dimension corresponded to better reservoir fracability.Fracture complexity was enhanced by a decrease in the horizontal principal stress difference or an increase in the injection rate.Under optimal conditions of a 3 MPa stress difference and an injection rate of 60 mL/min,fracability improved by 27.6%.Furthermore,liquid carbon dioxide(CO_(2))improved fracability by 50.7%compared to using water as the fracturing fluid,a result attributed to its low viscosity and strong diffusion capacity,which activated a greater number of natural fractures.A fracability evaluation model integrating brittleness,fracture toughness,and dimensionless net pressure was developed using regression analysis,which demonstrated high reliability with a strong determination coefficient(R^(2))of 0.9019.This study clarifies the logical relationships among fracture area,complexity,and fractal dimension,providing a novel method for evaluating the fracability of coal reservoirs.
文摘Optical coherence tomography(OCT),particularly Swept-Source OCT,is widely employed in medical diagnostics and industrial inspections owing to its high-resolution imaging capabilities.However,Swept-Source OCT 3D imaging often suffers from stripe artifacts caused by unstable light sources,system noise,and environmental interference,posing challenges to real-time processing of large-scale datasets.To address this issue,this study introduces a real-time reconstruction system that integrates stripe-artifact suppression and parallel computing using a graphics processing unit.This approach employs a frequency-domain filtering algorithm with adaptive anti-suppression parameters,dynamically adjusted through an image quality evaluation function and optimized using a convolutional neural network for complex frequency-domain feature learning.Additionally,a graphics processing unit integrated 3D reconstruction framework is developed,enhancing data processing throughput and real-time performance via a dual-queue decoupling mechanism.Experimental results demonstrate significant improvements in structural similarity(0.92),peak signal-to-noise ratio(31.62 dB),and stripe suppression ratio(15.73 dB)compared with existing methods.On the RTX 4090 platform,the proposed system achieved an end-to-end delay of 94.36 milliseconds,a frame rate of 10.3 frames per second,and a throughput of 121.5 million voxels per second,effectively suppressing artifacts while preserving image details and enhancing real-time 3D reconstruction performance.
基金supported by Kyonggi University Research Grant 2025.
文摘As large,room-scale environments become increasingly common,their spatial complexity increases due to variable,unstructured elements.Consequently,demand for room-scale service robots is surging,yet most technologies remain corridor-centric,and autonomous navigation in expansive rooms becomes unstable even around static obstacles.Existing approaches face several structural limitations.These include the labor-intensive requirement for large-scale object annotation and continual retraining,as well as the vulnerability of vanishing point or linebased methods when geometric cues are insufficient.In addition,the high cost of LiDAR and 3D perception errors caused by limited wall cues and dense interior clutter further limit their effectiveness.To address these challenges,we propose a zero-shot vision-based algorithm for robust 3D map reconstruction in geometry-deficient room-scale environments.The algorithm operates in three layers:Layer 1 performs dimension-wise boundary detection;Layer 2 estimates vanishing points,refines the precise perspective space,and extracts a floor mask;and Layer 3 conducts 3D spatial mapping and obstacle recognition.The proposed method was experimentally validated across various geometric-deficient room-scale environments,including lobbies,seminar rooms,conference rooms,cafeterias,and museums—demonstrating its ability to reliably reconstruct 3D maps and accurately recognize obstacles.Experimental results show that the proposed algorithm achieved an F1 score of 0.959 in precision perspective space detection and 0.965 in floor mask extraction.For obstacle recognition and classification,it obtained F1 scores of 0.980 in obstacle absent areas,0.913 in solid obstacle environments,and 0.939 in skeleton-type sparse obstacle environments,confirming its high precision and reliability in geometric-deficient room-scale environments.
基金National Key Scientific Instrument and Equipment Development Project under Grant No.61827801the open research fund of State Key Laboratory of Integrated Services Networks,No.ISN22-11+1 种基金Natural Science Foundation of Jiangsu Province,No.BK20211182open research fund of National Mobile Communications Research Laboratory,Southeast University,No.2022D04。
文摘Spectrum map construction,which is crucial in cognitive radio(CR)system,visualizes the invisible space of the electromagnetic spectrum for spectrum-resource management and allocation.Traditional reconstruction methods are generally for twodimensional(2D)spectrum map and driven by abundant sampling data.In this paper,we propose a data-model-knowledge-driven reconstruction scheme to construct the three-dimensional(3D)spectrum map under multi-radiation source scenarios.We firstly design a maximum and minimum path loss difference(MMPLD)clustering algorithm to detect the number of radiation sources in a 3D space.Then,we develop a joint location-power estimation method based on the heuristic population evolutionary optimization algorithm.Considering the variation of electromagnetic environment,we self-learn the path loss(PL)model based on the sampling data.Finally,the 3D spectrum is reconstructed according to the self-learned PL model and the extracted knowledge of radiation sources.Simulations show that the proposed 3D spectrum map reconstruction scheme not only has splendid adaptability to the environment,but also achieves high spectrum construction accuracy even when the sampling rate is very low.
文摘[Significance]In alignment with the national germplasm security strategy,current research efforts are accelerating the adoption of precision breeding in sheep.Within the whole-genome selection,accurate phenotyping of body morphometrics is critical for assessing growth performance and breeding value.Traditional manual measurements are inefficient,prone to human error,and may cause stress to sheep,limiting their suitability for precision sheep management.By summarizing the applications of sheep body size measurement technologies and analyzing their development directions,this paper provides theoretical references and practical guidance for the research and application of non contact sheep body size measurement.[Progress]This review synthesizes progress across three principal methodological paradigms:two-dimensional(2D)image-based techniques,three-dimensional(3D)point cloud-based approaches,and integrated 2D-3D fusion systems.2D methods,employing either handcrafted geometric features or deep learning-based keypoint detector algorithms,are cost-effective and operationally simple but sensitive to variation in imaging conditions and unable to capture critical circumference metrics.3D point-cloud approaches enable precise reconstruction of full animal morphology,supporting comprehensive body-size acquisition with higher accuracy,yet face challenges including high hardware costs,complex data workflows,and sensitivity to posture variability.Hybrid 2D-3D fusion systems combine semantic richness from RGB imagery with geometric completeness from point clouds.Having been effectively validated in other livestock specise,e.g.,cattle and pigs,these fusion systems have demonstrated excellent performance,providing important technical references and practical insights for sheep body size measurement.[Conclusions and Prospects]Firstly,future research should focus on constructing large-scale,high-quality datasets for sheep body size measurement that encompass diverse breeds,growth stages,and environmental conditions,thereby enhancing model robustness and generalization.Secondly,the development of lightweight artificial intelligence models is essential.Techniques such as model compression,quantization,and algorithmic optimization can substantially reduce computational complexity and storage requirements,facilitating deployment in resource-constrained environments.Thirdly,the 3D point cloud processing pipeline should be streamlined to improve the efficiency of data acquisition,filtering,registration,and segmentation,while promoting the integration of low-cost,high-resilience vision systems into practical farming scenarios.Fourthly,specific emphasis should be placed on improving the accuracy of curved-dimensional measurements,such as chest circumference,abdominal circumference,and shank circumference,through advances in pose standardization,refined 3D segmentation strategies,and multimodal data fusion.Finally,the cross-fertilization of sheep body size measurement technologies with analogous methods for other livestock species offers a promising pathway for mutual learning and collaborative innovation,accelerating the industrialization of automated sheep morphometric systems and supporting the development of intelligent,data-driven pasture management practices.
文摘目的:探究3D全腹腔镜根治术治疗Ⅲ期远端胃癌的疗效及安全性。方法:收集2021年2月至2024年2月收治的Ⅲ期远端胃癌患者的临床资料。将接受腹腔镜辅助远端胃癌根治术的患者纳入对照组,将接受3D全腹腔镜辅助远端胃癌根治术的患者纳入观察组,采用倾向性评分匹配法以最邻近匹配原则1∶1进行匹配,最终两组各纳入60例。比较两组手术情况、术后恢复情况、术后疼痛程度评分(VAS)、围手术期应激反应指标、胃肠功能指标及并发症发生情况。结果:与对照组相比,观察组切口长度、消化道重建时间、术后首次排气时间、自主下床活动时间及住院时间更短,使用镇痛药物患者比例更低,切口美容评分更高,术后并发症发生率更低(P<0.05)。术后8、24、48、72 h两组VAS均低于治疗前,且观察组低于对照组(P<0.05)。观察组术后24、72 h ACTH、PGE2升高幅度和MTL、GAS降低幅度均小于对照组(P<0.05)。结论:3D全腹腔镜根治术治疗Ⅲ期远端胃癌创伤小、术后并发症少,有利于胃肠功能早期恢复,且具有切口美观的优势。
文摘随着自动驾驶的发展,多传感器融合得到广泛应用。CLOCs是基于后融合的3D目标检测网络,但它对遮蔽物体的检测性能较差。针对此问题,提出一种融合双目测距和门控循环单元(Gated Recurrent Unit,GRU)的3D目标检测网络,其在CLOCs网络融合3D和2D的交并比(Intersection over Union,IoU)的基础上,在2D目标检测网络中引入双目测距来关联2D和3D的深度信息,在卷积之后加入GRU网络,用来捕捉时序数据的依赖关系。采用kitti数据集进行验证,实验结果表明检测精度得到了提升。
