Panoramic images, offering a 360-degree view, are essential in virtual reality(VR) and augmented reality(AR), enhancing realism with high-quality textures. However, acquiring complete and high-quality panoramic textur...Panoramic images, offering a 360-degree view, are essential in virtual reality(VR) and augmented reality(AR), enhancing realism with high-quality textures. However, acquiring complete and high-quality panoramic textures is challenging. This paper introduces a method using generative adversarial networks(GANs) and the contrastive language-image pretraining(CLIP) model to restore and control texture in panoramic images. The GAN model captures complex structures and maintains consistency, while CLIP enables fine-grained texture control via semantic text-image associations. GAN inversion optimizes latent codes for precise texture details. The resulting low dynamic range(LDR) images are converted to high dynamic range(HDR) using the Blender engine for seamless texture blending. Experimental results demonstrate the effectiveness and flexibility of this method in panoramic texture restoration and generation.展开更多
Deep neural networks provide accurate results for most applications.However,they need a big dataset to train properly.Providing a big dataset is a significant challenge in most applications.Image augmentation refers t...Deep neural networks provide accurate results for most applications.However,they need a big dataset to train properly.Providing a big dataset is a significant challenge in most applications.Image augmentation refers to techniques that increase the amount of image data.Common operations for image augmentation include changes in illumination,rotation,contrast,size,viewing angle,and others.Recently,Generative Adversarial Networks(GANs)have been employed for image generation.However,like image augmentation methods,GAN approaches can only generate images that are similar to the original images.Therefore,they also cannot generate new classes of data.Texture images presentmore challenges than general images,and generating textures is more complex than creating other types of images.This study proposes a gradient-based deep neural network method that generates a new class of texture.It is possible to rapidly generate new classes of textures using different kernels from pre-trained deep networks.After generating new textures for each class,the number of textures increases through image augmentation.During this process,several techniques are proposed to automatically remove incomplete and similar textures that are created.The proposed method is faster than some well-known generative networks by around 4 to 10 times.In addition,the quality of the generated textures surpasses that of these networks.The proposed method can generate textures that surpass those of someGANs and parametric models in certain image qualitymetrics.It can provide a big texture dataset to train deep networks.A new big texture dataset is created artificially using the proposed method.This dataset is approximately 2 GB in size and comprises 30,000 textures,each 150×150 pixels in size,organized into 600 classes.It is uploaded to the Kaggle site and Google Drive.This dataset is called BigTex.Compared to other texture datasets,the proposed dataset is the largest and can serve as a comprehensive texture dataset for training more powerful deep neural networks and mitigating overfitting.展开更多
manufacturing(AM)technologies have been recognized for their capability to build complex components and hence have ofered more freedom to designers for a long time.The ability to directly use a computer-aided design(C...manufacturing(AM)technologies have been recognized for their capability to build complex components and hence have ofered more freedom to designers for a long time.The ability to directly use a computer-aided design(CAD)model has allowed for fabricating and realizing complicated components,monolithic design,reducing the number of components in an assembly,decreasing time to market,and adding performance or comfort-enhancing functionalities.One of the features that can be introduced for boosting a component functionality using AM is the inclusion of surface texture on a given component.This inclusion is usually a difcult task as creating a CAD model resolving fne details of a given texture is difcult even using commercial software packages.This paper develops a methodology to include texture directly on the CAD model of a target surface using a patch-based sampling texture synthesis algorithm,which can be manufactured using AM.Input for the texture generation algorithm can be either a physical sample or an image with heightmap information.The heightmap information from a physical sample can be obtained by 3D scanning the sample and using the information from the acquired point cloud.After obtaining the required inputs,the patches are sampled for texture generation according to non-parametric estimation of the local conditional Markov random feld(MRF)density function,which helps avoid mismatched features across the patch boundaries.While generating the texture,a design constraint to ensure AM producibility is considered,which is essential when manufacturing a component using,e.g.,Fused Deposition Melting(FDM)or Laser Powder Bed Fusion(LPBF).The generated texture is then mapped onto the surface using the developed distance and angle preserving mapping algorithms.The implemented algorithms can be used to map the generated texture onto a mathematically defned surface.This paper maps the textures onto fat,curved,and sinusoidal surfaces for illustration.After the texture mapping,a stereolithography(STL)model is generated with the desired texture on the target surface.The generated STL model is printed using FDM technology as a fnal step.展开更多
Tailored surface textures at the micro- or nanoscale dimensions are widely used to get required functional performances. Rotary ultrasonic texturing （RUT） technique has been proved to be capable of fabricating perio...Tailored surface textures at the micro- or nanoscale dimensions are widely used to get required functional performances. Rotary ultrasonic texturing （RUT） technique has been proved to be capable of fabricating periodic micro- and nanostructures. In the present study, diamond tools with geometrically defined cutting edges were designed for fabricating different types of tailored surface textures using the RUT method. Surface generation mechanisms and machinable structures of the RUT process are analyzed and simulated with a 3D-CAD program. Textured surfaces generated by using a triangular pyramid cutting tip are constructed. Different textural patterns from several micrometers to several tens of micrometers with few burrs were successfully fabricated, which proved that tools with a proper two-rake-face design are capable of removing cutting chips efficiently along a sinusoidal cutting locus in the RUT process. Technical applications of the textured surfaces are also discussed. Wetting properties of textured aluminum surfaces were evaluated by combining the test of surface roughness features. The results show that the real surface area of the textured aluminum surfaces almost doubled by comparing with that of a flat surface, and anisotropic wetting properties were obtained due to the obvious directional textural features.展开更多
Mud is a ubiquitous building material in Nigeria,perhaps this is the reason why it is hardly seen as the outright building material that it is.The most popular contribution of mud to Nigerian architecture can only be ...Mud is a ubiquitous building material in Nigeria,perhaps this is the reason why it is hardly seen as the outright building material that it is.The most popular contribution of mud to Nigerian architecture can only be seen in the ancient traditional huts all over the country.Although still a building material in the suburbs of the country,mud is seen as a relic of the past,a symbol of a primitive tale of Nigerian building construction.The primary effort here is to redefine mud as a“skin”with infinite possibilities of imagery and texture,rather than its typical application as a wall in Nigerian architecture.Mud is attempted to be expressed via a new geometric vocabulary by re-evaluating its surreptitious properties including its ability to behave like a formally defined NURBS(non-uniform rational basis spline)surface.The properties of mud and clay are unconventionally simulated in computer modelling and analysis software to understand the ways in which it can be optimized for advanced building applications.Streamlined calculations and algorithmic calculations serve as tools to discover the NURBS-propensity of mud.This provides a whole new low-cost construction opportunity for the building of irregularly flowing structures.展开更多
Building damage maps after disasters can help us to better manage the rescue operations.Researchers have used Light Detection and Ranging(LiDAR)data for extracting the building damage maps.For producing building damag...Building damage maps after disasters can help us to better manage the rescue operations.Researchers have used Light Detection and Ranging(LiDAR)data for extracting the building damage maps.For producing building damage maps from LiDAR data in a rapid manner,it is necessary to understand the effectiveness of features and classifiers.However,there is no comprehensive study on the performance of features and classifiers in identifying damaged areas.In this study,the effectiveness of three texture extraction methods and three fuzzy systems for producing the building damage maps was investigated.In the proposed method,at first,a pre-processing stage was utilized to apply essential processes on post-event LiDAR data.Second,textural features were extracted from the pre-processed LiDAR data.Third,fuzzy inference systems were generated to make a relation between the extracted textural features of buildings and their damage extents.The proposed method was tested across three areas over the 2010 Haiti earthquake.Three building damage maps with overall accuracies of 75.0%,78.1%and 61.4%were achieved.Based on outcomes,the fuzzy inference systems were stronger than random forest,bagging,boosting and support vector machine classifiers for detecting damaged buildings.展开更多
Three-dimensional(3D)reconstruction of a human head with high precision has promising applications in scientific research,product design and other fields.However,it still faces resistance from two factors.One is inacc...Three-dimensional(3D)reconstruction of a human head with high precision has promising applications in scientific research,product design and other fields.However,it still faces resistance from two factors.One is inaccurate registration caused by symmetrical distribution of head feature points,and the other is economic burden due to highaccuracy sensors.Research on 3D reconstruction with portable consumer RGB-D sensors such as the Microsoft Kinect has been highlighted in recent years.Based on our multi-Kinect system,a precise and low-cost three-dimensional modeling method and its system implementation are introduced in this paper.A registration method for multisource point clouds is provided,which can reduce the fusion differences and reconstruct the head model accurately.In addition,a template-based texture generation algorithm is presented to generate a fine texture.The comparison and analysis of our experiments show that our method can reconstruct a head model in an acceptable time with less memory and better effect.展开更多
文摘Panoramic images, offering a 360-degree view, are essential in virtual reality(VR) and augmented reality(AR), enhancing realism with high-quality textures. However, acquiring complete and high-quality panoramic textures is challenging. This paper introduces a method using generative adversarial networks(GANs) and the contrastive language-image pretraining(CLIP) model to restore and control texture in panoramic images. The GAN model captures complex structures and maintains consistency, while CLIP enables fine-grained texture control via semantic text-image associations. GAN inversion optimizes latent codes for precise texture details. The resulting low dynamic range(LDR) images are converted to high dynamic range(HDR) using the Blender engine for seamless texture blending. Experimental results demonstrate the effectiveness and flexibility of this method in panoramic texture restoration and generation.
基金supported via funding from Prince Sattam bin Abdulaziz University(PSAU/2025/R/1446)Princess Nourah bint Abdulrahman University(PNURSP2025R300)Prince Sultan University.
文摘Deep neural networks provide accurate results for most applications.However,they need a big dataset to train properly.Providing a big dataset is a significant challenge in most applications.Image augmentation refers to techniques that increase the amount of image data.Common operations for image augmentation include changes in illumination,rotation,contrast,size,viewing angle,and others.Recently,Generative Adversarial Networks(GANs)have been employed for image generation.However,like image augmentation methods,GAN approaches can only generate images that are similar to the original images.Therefore,they also cannot generate new classes of data.Texture images presentmore challenges than general images,and generating textures is more complex than creating other types of images.This study proposes a gradient-based deep neural network method that generates a new class of texture.It is possible to rapidly generate new classes of textures using different kernels from pre-trained deep networks.After generating new textures for each class,the number of textures increases through image augmentation.During this process,several techniques are proposed to automatically remove incomplete and similar textures that are created.The proposed method is faster than some well-known generative networks by around 4 to 10 times.In addition,the quality of the generated textures surpasses that of these networks.The proposed method can generate textures that surpass those of someGANs and parametric models in certain image qualitymetrics.It can provide a big texture dataset to train deep networks.A new big texture dataset is created artificially using the proposed method.This dataset is approximately 2 GB in size and comprises 30,000 textures,each 150×150 pixels in size,organized into 600 classes.It is uploaded to the Kaggle site and Google Drive.This dataset is called BigTex.Compared to other texture datasets,the proposed dataset is the largest and can serve as a comprehensive texture dataset for training more powerful deep neural networks and mitigating overfitting.
基金Supported by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)-EXC 2023 Internet of Production/390621612。
文摘manufacturing(AM)technologies have been recognized for their capability to build complex components and hence have ofered more freedom to designers for a long time.The ability to directly use a computer-aided design(CAD)model has allowed for fabricating and realizing complicated components,monolithic design,reducing the number of components in an assembly,decreasing time to market,and adding performance or comfort-enhancing functionalities.One of the features that can be introduced for boosting a component functionality using AM is the inclusion of surface texture on a given component.This inclusion is usually a difcult task as creating a CAD model resolving fne details of a given texture is difcult even using commercial software packages.This paper develops a methodology to include texture directly on the CAD model of a target surface using a patch-based sampling texture synthesis algorithm,which can be manufactured using AM.Input for the texture generation algorithm can be either a physical sample or an image with heightmap information.The heightmap information from a physical sample can be obtained by 3D scanning the sample and using the information from the acquired point cloud.After obtaining the required inputs,the patches are sampled for texture generation according to non-parametric estimation of the local conditional Markov random feld(MRF)density function,which helps avoid mismatched features across the patch boundaries.While generating the texture,a design constraint to ensure AM producibility is considered,which is essential when manufacturing a component using,e.g.,Fused Deposition Melting(FDM)or Laser Powder Bed Fusion(LPBF).The generated texture is then mapped onto the surface using the developed distance and angle preserving mapping algorithms.The implemented algorithms can be used to map the generated texture onto a mathematically defned surface.This paper maps the textures onto fat,curved,and sinusoidal surfaces for illustration.After the texture mapping,a stereolithography(STL)model is generated with the desired texture on the target surface.The generated STL model is printed using FDM technology as a fnal step.
基金Supported by Japan Society for the Promotion of Science(Grant Nos.14J04115,16K17990)
文摘Tailored surface textures at the micro- or nanoscale dimensions are widely used to get required functional performances. Rotary ultrasonic texturing （RUT） technique has been proved to be capable of fabricating periodic micro- and nanostructures. In the present study, diamond tools with geometrically defined cutting edges were designed for fabricating different types of tailored surface textures using the RUT method. Surface generation mechanisms and machinable structures of the RUT process are analyzed and simulated with a 3D-CAD program. Textured surfaces generated by using a triangular pyramid cutting tip are constructed. Different textural patterns from several micrometers to several tens of micrometers with few burrs were successfully fabricated, which proved that tools with a proper two-rake-face design are capable of removing cutting chips efficiently along a sinusoidal cutting locus in the RUT process. Technical applications of the textured surfaces are also discussed. Wetting properties of textured aluminum surfaces were evaluated by combining the test of surface roughness features. The results show that the real surface area of the textured aluminum surfaces almost doubled by comparing with that of a flat surface, and anisotropic wetting properties were obtained due to the obvious directional textural features.
文摘Mud is a ubiquitous building material in Nigeria,perhaps this is the reason why it is hardly seen as the outright building material that it is.The most popular contribution of mud to Nigerian architecture can only be seen in the ancient traditional huts all over the country.Although still a building material in the suburbs of the country,mud is seen as a relic of the past,a symbol of a primitive tale of Nigerian building construction.The primary effort here is to redefine mud as a“skin”with infinite possibilities of imagery and texture,rather than its typical application as a wall in Nigerian architecture.Mud is attempted to be expressed via a new geometric vocabulary by re-evaluating its surreptitious properties including its ability to behave like a formally defined NURBS(non-uniform rational basis spline)surface.The properties of mud and clay are unconventionally simulated in computer modelling and analysis software to understand the ways in which it can be optimized for advanced building applications.Streamlined calculations and algorithmic calculations serve as tools to discover the NURBS-propensity of mud.This provides a whole new low-cost construction opportunity for the building of irregularly flowing structures.
文摘Building damage maps after disasters can help us to better manage the rescue operations.Researchers have used Light Detection and Ranging(LiDAR)data for extracting the building damage maps.For producing building damage maps from LiDAR data in a rapid manner,it is necessary to understand the effectiveness of features and classifiers.However,there is no comprehensive study on the performance of features and classifiers in identifying damaged areas.In this study,the effectiveness of three texture extraction methods and three fuzzy systems for producing the building damage maps was investigated.In the proposed method,at first,a pre-processing stage was utilized to apply essential processes on post-event LiDAR data.Second,textural features were extracted from the pre-processed LiDAR data.Third,fuzzy inference systems were generated to make a relation between the extracted textural features of buildings and their damage extents.The proposed method was tested across three areas over the 2010 Haiti earthquake.Three building damage maps with overall accuracies of 75.0%,78.1%and 61.4%were achieved.Based on outcomes,the fuzzy inference systems were stronger than random forest,bagging,boosting and support vector machine classifiers for detecting damaged buildings.
基金This work is partly supported by the National Defense Pre-Research Foundation of China(61400010102).
文摘Three-dimensional(3D)reconstruction of a human head with high precision has promising applications in scientific research,product design and other fields.However,it still faces resistance from two factors.One is inaccurate registration caused by symmetrical distribution of head feature points,and the other is economic burden due to highaccuracy sensors.Research on 3D reconstruction with portable consumer RGB-D sensors such as the Microsoft Kinect has been highlighted in recent years.Based on our multi-Kinect system,a precise and low-cost three-dimensional modeling method and its system implementation are introduced in this paper.A registration method for multisource point clouds is provided,which can reduce the fusion differences and reconstruct the head model accurately.In addition,a template-based texture generation algorithm is presented to generate a fine texture.The comparison and analysis of our experiments show that our method can reconstruct a head model in an acceptable time with less memory and better effect.
