This paper reports improvement of immersive scene on an augmented reality system that handles virtual realizations of buildings and civil engineering structures at real scale in outdoor settings . The authors employed : 1 ) 3D shape reconstruction from multi-viewpoint images for resolving occlusion between virtual objects and real objects and rendering shadow on virtual objects . 2 ) Some functions such as corrects GPS and 3 DOF inertial measurement unit outputs for accurate geometric registration . Three-dimensional ( 3D ) shape reconstruction from one or multiple observations is a primary problem of computer vision . Shape from Focus ( SFF ) is a passive optical method that uses multiple two-dimensional ( 2D ) images with different focus levels . AbstractThis paper provides an introduction to photometric methods for image-based 3D shape reconstruction and a survey of photometric stereo techniques . We begin with taxonomy of active and passive shape acquisition techniques . Single-view 3D shape reconstruction is an important but challenging problem , mainly for two reasons . First , as shape annotation is very expensive to acquire , current methods rely on synthetic data , in which ground-truth 3D annotation is easy to obtain . Developing such a description requires both bottom-up , data driven processes that associate symbolic knowledge representation structures with the data coming out of a vision system , and top-down processes in which high level , symbolic information is in its turn employed to drive and further refine the interpretation of a scene . On the one hand , the computer vision community approached this problem in terms of 2D/3D shape reconstruction and of estimation of motion parameters . On the other , the AI community developed rich and expressive systems for the description of processes , events , actions and , in general , of dynamic situations . This thesis presents data processing techniques for three different but related application areas : embedding learning for classification , fusion of low bit depth images and 3D reconstruction from 2D images . For embedding learning for classification , a novel manifold embedding method is proposed for the automated processing of large , varied data sets . Since this data model has known disadvantages , a more practical solution of visualising such data involves the creation of solid three-dimensional meshes composed of edges and facets . In this paper , several methods for 3D shape reconstruction of data obtained by multibeam and LiDAR measurements are presented , based on the application of various pre‑processing algorithms . The results , obtained by combining the proposed data processing methods with several different surface reconstruction algorithms , are presented and the advantages of each method are highlighted . In this paper , we introduce 3D-GMNet , a deep neural network for single-image 3D shape recovery . As the name suggests , 3D-GMNet recovers 3D shape as a Gaussian mixture model . We present a method to learn single-view reconstruction of the 3D shape , pose , and texture of objects from categorized natural images in a self-supervised manner . Since this is a severely ill-posed problem , carefully designing a training method and introducing constraints are essential . We show how the related task of 3D facial landmark localisation can be incorporated into the proposed framework and help improve reconstruction quality , especially for the cases of large poses and facial expressions . We report results for a large number of experiments on both controlled and completely unconstrained images from the web , illustrating that our method outperforms prior work on single image 3D face reconstruction by a large margin . section : Closely related work Our final solution is a conditional shape sampler , capable of predicting multiple plausible 3D point clouds from an input image . In experiments not only can our system outperform state - ofthe - art methods on single image based 3d reconstruction benchmarks ; but it also shows strong performance for 3d shape completion and promising ability in making multiple plausible predictions . section : Introduction Firstly , we divide the large data points into three categories : flat point set , high curvature point set , and boundary point set . The errors of topological structure for 3D meshes usually occur in the place with large curvatures and noise points , so the division of high curvature point set is beneficial to solve the low-accuracy problem in 3D mesh reconstruction . Moreover , the flat points can be treated as one kind of point to avoid repetitive calculations because their features are basically the same . In many scenarios , such as autonomous driving , only the overall outline of the model is required . Finally , during the 3D mesh reconstruction , the inner edge adjacency list and optimal selection principle are set to improve the robustness of the whole system . Simulation experiments show that the proposed 3D mesh reconstruction can naturally reflect the detailed features of objects in the big data platform , especially effective for the scattered point cloud . Abstract In this , The First Lady of Emerging Technologies coins a new term - “ Internet of 3D Printed Products " . She is showing us a third kind emerging future she is coining as : “ Internet of 3D Printed Products " . The real life monitoring and managing enabled by Internet of people has been brought to us by various Machine to Machine ( M2 M ) technologies . Now , we are looking at a third kind emerging future I am coining as : “ Internet of 3D Printed Products " . In “ Internet of 3D Printed Products , ” we are looking at not only intangible services , but tangible goods will be delivered as well to our computers over the Internet . Cave Automatic Virtual Environment ( CAVE ) provides an immersive virtual environment for 3D city planning . However , the user in the cave has to wear wearable markers for the interaction with the 3D models . There are several different ways to create and control virtual 3D ( three-dimensional ) models of robots , however , most of these methods can only be implemented or understood by experts with years of extensive experience in 3D graphics programming , 3D mathematics and a plethora of advanced skills in the areas of solid modelling and 3D animation . This paper presents a brief history and summary of the state-of-the-art in 3D game development tools and technologies which can be used to develop realistic looking graphics for developing user interfaces and robot control programming tools . Three-dimensional ( 3D ) city building models with its related information is an important part in the construction of Digital City and Digital Earth , and has been becoming a kind of staple resources on the web . Based on the characteristics of 3D building model , a service-oriented architecture and Web service technology for 3D city building models is discussed . To the best of our knowledge , this is the first time that spatial transformers have been described for projective geometry . The source code will be made public upon publication of this manuscript and we hope that our developments will benefit related 3D research applications . Automatic reconstruction of 3D terrain models by a digital photogrammetric station " Delta " , which is now under development in the Ukraine , is discussed . It is based on a previously proposed symmetric intensity-based dynamic programming technique . The main advantage of the proposed method lies in the fact that the change detection is performed by comparing line segments , and not surfaces or gray levels . Computing 3D surfaces in an image can be computationally intensive , and other methods such as image subtraction or cross-correlation are sensitive to lighting conditions and changes in viewpoints . Moreover , most man-made objects such as buildings , cars , and even cities viewed from above consist mainly of straight linesman-made objects , and therefore this method is highly applicable for such structures . We propose a generic method for the automatic multiple-organ segmentation of 3D images based on a multilabel graph cut optimization approach which uses location likelihood of organs and prior information of spatial relationships between them . The latter is derived from shortest-path constraints defined on the adjacency graph of structures and the former is defined by probabilistic atlases learned from a training dataset . The automatic extraction of animal 3D pose from images without markers is of interest in a range of scientific fields . Most work to date predicts animal pose from RGB images , based on 2D labelling of joint positions . Successful adaptation of these frameworks range from a focus in natural language and speech processing to realistic image synthesis , yielding promising , positive results . Nevertheless , very little work , outside of , has focused on modeling 3D objects , where generative architectures could be used to learn probabilistic embeddings . The model proposed in this paper will offer another step towards constructing powerful generative models of 3D structures . It is also used in our previous work for scene labeling . subsection : 3D Translation Estimation As illustrated in Fig . 3D pose from images : Most approaches to 3D pose inference directly from images fall into one of two categories : ( i ) models that learn to regress the 3D pose directly from image features and ( ii ) pipeline approaches where the 2D pose is first estimated , typically using discriminatively trained part models or joint predictors , and then lifted into 3D. While regression based methods suffer from the need to annotate all images with ground truth 3D poses – a technically complex and elaborate process – for pipeline Training a single network takes around 30 min per epoch , and around 17 hours in total . The segmentation of a 3D MR scan requires 10 seconds per CNN model , on average . On a single GPU , segmenting a new subject with the ensemble of CNNs takes around 100 seconds . Thus before influencing the signal , any deviation is amplified by which is exponential in the number of dimensions . For this reason the problem affects training of 3D CNNs more severely than conventional 2D systems . For countering it , we adopt the recently proposed Batch Normalisation ( BN ) technique to all hidden layers ( ) , which allows normalization of the FM activations at every optimization step in order to better preserve the signal . document : 3D Face Morphable Models “ In - the - Wild ” 3D Morphable Models ( 3DMMs ) are powerful statistical models of 3D facial shape and texture , and among the state - of - the - art methods for reconstructing facial shape from single images . With the advent of new 3D sensors , many 3D facial datasets have been collected containing both neutral as well as expressive faces . We present Face Swapping GAN ( FSGAN ) for face swapping and reenactment . Unlike previous work , FSGAN is subject agnostic and can be applied to pairs of faces without requiring training on those faces . To this end , we describe a number of technical contributions . We derive a novel recurrent neural network (RNN)-based approach for face reenactment which adjusts for both pose and expression variations and can be applied to a single image or a video sequence . For video sequences , we introduce continuous interpolation of the face views based on reenactment , Delaunay Triangulation , and barycentric coordinates . Given the pose information , the first CNN predicts a dynamic texture map that contains time-coherent high-frequency details , and the second CNN conditions the generation of the final video on the temporally coherent output of the first CNN . We demonstrate several applications of our approach , such as human reenactment and novel view synthesis from monocular video , where we show significant improvement over the state of the art both qualitatively and quantitatively . Given the pose information , the first CNN predicts a dynamic texture map that contains time-coherent high-frequency details , and the second CNN conditions the generation of the final video on the temporally coherent output of the first CNN . We demonstrate several applications of our approach , such as human reenactment and novel view synthesis from monocular video , where we show significant improvement over the state of the art both qualitatively and quantitatively . In this work , we propose an encoder-decoder-like architecture to perform face reenact- ment in image sequences . Our goal is to transfer the training subject identity to a given test subject , regularazing the generation with Action units and gaze vectors to generate more lifelike results . Animating a static face image with target facial expressions and movements is important in the area of image editing and movie production . This face reenactment process is challenging due to the complex geometry and movement of human faces . Previous work usually requires a large set of images from the same person to model the appearance . Recent face reenactment studies have achieved remarkable success either between two identities or in the many-to-one task . However , existing methods have limited scalability when the target person is not a predefined specific identity . Abstract This article summarizes the dissertation “ Face2Face : Realtime Facial Reenactment ” by Justus Thies ( Eurographics Graphics Dissertation Online , 2017 ) . It shows advances in the field of 3D reconstruction of human faces using commodity hardware . g . , gender and expression , can not only provide a natural description of facial images , but also offer a unified viewpoint for understanding many facial animation and manipulation tasks . For example , the goal of facial avatar and reenactment is to transfer the facial expression attributes of a source actor to a target actor . In most applications such as expression transfer , accessory removal and age progression , the animation only modifies the related attribute without changing the identity . The effectiveness of Mutual Information ( MI ) as a technique for automated multi-sensor image registration has previously been demonstrated for medical and remote sensing applications . In this paper , a new General Weighted MI ( GWMI ) approach that improves the robustness of MI to local maxima , particularly in the case of registering optical imagery and 3D point clouds , is presented . Two different methods including a Gaussian Mixture Model ( GMM ) and Kernel Density Estimation have been used to define the weight function of joint probability , regardless of the modality of the data being registered . Analogous to image based convolutional architectures , recently introduced graph based architectures afford similar filtering and pooling operations on arbitrary graphs . We adopt these graph based methods to 3D point clouds to introduce a generic vector representation of 3D graphs , we call graph 3D ( G3D ) . We believe we are the first to use large scale transfer learning on 3D point cloud data and demonstrate the discriminant power of our salient latent representation of 3D point clouds on unforeseen test sets . That is , if the captured depth image contains around 50 thousand points in the cloud for a single pose for one individual , then the storage size of the entire dataset will be in order of giga if not tera bytes . With these motivations , this work introduces a new technique for 3D point clouds generation using a neural modeling system to handle the differences caused by heterogeneous depth cameras , and to generate a new face canonical compact representation . The proposed system reduces the stored 3D dataset size , and if required , provides an accurate dataset regeneration . First , recognition level of aerial object is obtained for task requirement analysis . Then a scheme of generating 3D point clouds is given to acquire data of 3D scannerless imaging LiDAR . By applying Zernike moment 's feature extraction algorithm and correlation coefficient method , the data features of the simulated point cloud is extracted and recognized to decide if the recognition results satisfy the task requirement . Objects of different sizes can be modelled using image sequences from commercial digital cameras that are processed by web services and freely available software packages , forming low cost systems for numerous applications ( restoration , historical care of monuments , visualization , analysis of the state of construction and the damage , etc . ) . In this contribution various cultural objects ( historical buildings , statues/figures , archaeological finds , etc . ) have been reconstructed in order to investigate the potential of this technology which enables the automatic generation of 3D point clouds or surface models ( as 3D polygons ) with photo-realistic texture from image data . These so-called low cost systems represent an efficient alternative to expensive terrestrial laser scanning syst ... The objective of the study is to assess the accuracy of 3D models generated by computer vision automated web-based applications ( such as Autodesk 123D Catch ) and compare the output to 3D models produced by optical technologies using multi-image photogrammetry and commercial 3D scanner . The web-based application enables the automatic generation of 3D point clouds or surface models ( as 3D polygons ) with photo-realistic texture from image data thus represent an efficient alternative to expensive terrestrial laser scanning systems for the as-built documentation of 3D objects in cultural heritage . The output of the study will be in the form of 3D replication of the Petalaindera , using 3D print technology . However , this approach has limitations in the construction industry due to expensive and fragile equipments , lack of portability and need of trained operators . This study aims at investigating an alternate technology to generate as-built 3D point clouds using photos taken using handheld digital cameras , compare them against the original as-built 3D models , and check for accuracy of the modeling process . This analysis can aid in a more reliable and effective decision making due to its cost effectiveness and ease of use , particularly in heavy infrastructure projects which are continually undergoing rehabilitation work . Abstract Over-segmentation , or super-pixel generation , is a common preliminary stage for many computer vision applications . New acquisition technologies enable the capturing of 3D point clouds that contain color and geometrical information . This 3D information can be utilized to improve the results of over-segmentation , which uses mainly color information , and to generate clusters of points we call super-points . 3D generative shape modeling is a fundamental research area in computer vision and interactive computer graphics , with many real-world applications . This paper investigates the novel problem of generating 3D shape point cloud geometry from a symbolic part tree representation . In order to learn such a conditional shape generation procedure in an end-to-end fashion , we propose a conditional GAN " part tree"-to-"point cloud " model ( PT2PC ) that disentangles the structural and geometric factors . Abstract The article presents a robot 3-D scanning system for generation of 3-D point clouds of an object by using multi-view 3-D scanning and novel data registration . Our approach mainly comprises two important elements in the determination of next best probe pose and multiple-view point clouds registration . Selection is a fundamental task in exploratory analysis and visualization of 3D point clouds . Prior researches on selection methods were developed mainly based on heuristics such as local point density , thus limiting their applicability in general data . We can then easily locate the ego - lane boundaries using a few heuristic filters . Noting that lane boundaries on highways are usually marked with retro - reflective materials , we first filter out low - reflectivity surfaces such as asphalt in our 3D point cloud maps and only consider points with high enough laser return intensities . We then filter out other reflective surfaces such as cars and traffic signs by only considering points whose heights are close enough the ground plane . Driven by this unorthodox output form and the inherent ambiguity in groundtruth , we design architecture , loss function and learning paradigm that are novel and effective . Our final solution is a conditional shape sampler , capable of predicting multiple plausible 3D point clouds from an input image . In experiments not only can our system outperform state - ofthe - art methods on single image based 3d reconstruction benchmarks ; but it also shows strong performance for 3d shape completion and promising ability in making multiple plausible predictions . We tried octree based solution to increase the volume resolution , but found it still hard to recover surface level details as much as our model . PSG produces sparse 3D point clouds , and it is non - trivial to recover meshes from them . This is due to the applied Chamfer loss acting like a regression loss which gives too much degree of freedom to the point cloud .
This paper reports improvement of immersive scene on an augmented reality system that handles virtual realizations of buildings and civil engineering structures at real scale in outdoor settings . The authors employed : 1 ) 3D shape reconstruction from multi-viewpoint images for resolving occlusion between virtual objects and real objects and rendering shadow on virtual objects . 2 ) Some functions such as corrects GPS and 3 DOF inertial measurement unit outputs for accurate geometric registration .
Three-dimensional ( 3D ) shape reconstruction from one or multiple observations is a primary problem of computer vision . Shape from Focus ( SFF ) is a passive optical method that uses multiple two-dimensional ( 2D ) images with different focus levels .
AbstractThis paper provides an introduction to photometric methods for image-based 3D shape reconstruction and a survey of photometric stereo techniques . We begin with taxonomy of active and passive shape acquisition techniques .
Single-view 3D shape reconstruction is an important but challenging problem , mainly for two reasons . First , as shape annotation is very expensive to acquire , current methods rely on synthetic data , in which ground-truth 3D annotation is easy to obtain .
Developing such a description requires both bottom-up , data driven processes that associate symbolic knowledge representation structures with the data coming out of a vision system , and top-down processes in which high level , symbolic information is in its turn employed to drive and further refine the interpretation of a scene . On the one hand , the computer vision community approached this problem in terms of 2D/3D shape reconstruction and of estimation of motion parameters . On the other , the AI community developed rich and expressive systems for the description of processes , events , actions and , in general , of dynamic situations .
This thesis presents data processing techniques for three different but related application areas : embedding learning for classification , fusion of low bit depth images and 3D reconstruction from 2D images . For embedding learning for classification , a novel manifold embedding method is proposed for the automated processing of large , varied data sets .
Since this data model has known disadvantages , a more practical solution of visualising such data involves the creation of solid three-dimensional meshes composed of edges and facets . In this paper , several methods for 3D shape reconstruction of data obtained by multibeam and LiDAR measurements are presented , based on the application of various pre‑processing algorithms . The results , obtained by combining the proposed data processing methods with several different surface reconstruction algorithms , are presented and the advantages of each method are highlighted .
In this paper , we introduce 3D-GMNet , a deep neural network for single-image 3D shape recovery . As the name suggests , 3D-GMNet recovers 3D shape as a Gaussian mixture model .
We present a method to learn single-view reconstruction of the 3D shape , pose , and texture of objects from categorized natural images in a self-supervised manner . Since this is a severely ill-posed problem , carefully designing a training method and introducing constraints are essential .
We show how the related task of 3D facial landmark localisation can be incorporated into the proposed framework and help improve reconstruction quality , especially for the cases of large poses and facial expressions . We report results for a large number of experiments on both controlled and completely unconstrained images from the web , illustrating that our method outperforms prior work on single image 3D face reconstruction by a large margin . section : Closely related work
Our final solution is a conditional shape sampler , capable of predicting multiple plausible 3D point clouds from an input image . In experiments not only can our system outperform state - ofthe - art methods on single image based 3d reconstruction benchmarks ; but it also shows strong performance for 3d shape completion and promising ability in making multiple plausible predictions . section : Introduction
Firstly , we divide the large data points into three categories : flat point set , high curvature point set , and boundary point set . The errors of topological structure for 3D meshes usually occur in the place with large curvatures and noise points , so the division of high curvature point set is beneficial to solve the low-accuracy problem in 3D mesh reconstruction . Moreover , the flat points can be treated as one kind of point to avoid repetitive calculations because their features are basically the same .
In many scenarios , such as autonomous driving , only the overall outline of the model is required . Finally , during the 3D mesh reconstruction , the inner edge adjacency list and optimal selection principle are set to improve the robustness of the whole system . Simulation experiments show that the proposed 3D mesh reconstruction can naturally reflect the detailed features of objects in the big data platform , especially effective for the scattered point cloud .
Abstract In this , The First Lady of Emerging Technologies coins a new term - “ Internet of 3D Printed Products " . She is showing us a third kind emerging future she is coining as : “ Internet of 3D Printed Products " .
The real life monitoring and managing enabled by Internet of people has been brought to us by various Machine to Machine ( M2 M ) technologies . Now , we are looking at a third kind emerging future I am coining as : “ Internet of 3D Printed Products " . In “ Internet of 3D Printed Products , ” we are looking at not only intangible services , but tangible goods will be delivered as well to our computers over the Internet .
Cave Automatic Virtual Environment ( CAVE ) provides an immersive virtual environment for 3D city planning . However , the user in the cave has to wear wearable markers for the interaction with the 3D models .
There are several different ways to create and control virtual 3D ( three-dimensional ) models of robots , however , most of these methods can only be implemented or understood by experts with years of extensive experience in 3D graphics programming , 3D mathematics and a plethora of advanced skills in the areas of solid modelling and 3D animation . This paper presents a brief history and summary of the state-of-the-art in 3D game development tools and technologies which can be used to develop realistic looking graphics for developing user interfaces and robot control programming tools .
Three-dimensional ( 3D ) city building models with its related information is an important part in the construction of Digital City and Digital Earth , and has been becoming a kind of staple resources on the web . Based on the characteristics of 3D building model , a service-oriented architecture and Web service technology for 3D city building models is discussed .
To the best of our knowledge , this is the first time that spatial transformers have been described for projective geometry . The source code will be made public upon publication of this manuscript and we hope that our developments will benefit related 3D research applications .
Automatic reconstruction of 3D terrain models by a digital photogrammetric station " Delta " , which is now under development in the Ukraine , is discussed . It is based on a previously proposed symmetric intensity-based dynamic programming technique .
The main advantage of the proposed method lies in the fact that the change detection is performed by comparing line segments , and not surfaces or gray levels . Computing 3D surfaces in an image can be computationally intensive , and other methods such as image subtraction or cross-correlation are sensitive to lighting conditions and changes in viewpoints . Moreover , most man-made objects such as buildings , cars , and even cities viewed from above consist mainly of straight linesman-made objects , and therefore this method is highly applicable for such structures .
We propose a generic method for the automatic multiple-organ segmentation of 3D images based on a multilabel graph cut optimization approach which uses location likelihood of organs and prior information of spatial relationships between them . The latter is derived from shortest-path constraints defined on the adjacency graph of structures and the former is defined by probabilistic atlases learned from a training dataset .
The automatic extraction of animal 3D pose from images without markers is of interest in a range of scientific fields . Most work to date predicts animal pose from RGB images , based on 2D labelling of joint positions .
Successful adaptation of these frameworks range from a focus in natural language and speech processing to realistic image synthesis , yielding promising , positive results . Nevertheless , very little work , outside of , has focused on modeling 3D objects , where generative architectures could be used to learn probabilistic embeddings . The model proposed in this paper will offer another step towards constructing powerful generative models of 3D structures .
It is also used in our previous work for scene labeling . subsection : 3D Translation Estimation As illustrated in Fig .
3D pose from images : Most approaches to 3D pose inference directly from images fall into one of two categories : ( i ) models that learn to regress the 3D pose directly from image features and ( ii ) pipeline approaches where the 2D pose is first estimated , typically using discriminatively trained part models or joint predictors , and then lifted into 3D. While regression based methods suffer from the need to annotate all images with ground truth 3D poses – a technically complex and elaborate process – for pipeline
Training a single network takes around 30 min per epoch , and around 17 hours in total . The segmentation of a 3D MR scan requires 10 seconds per CNN model , on average . On a single GPU , segmenting a new subject with the ensemble of CNNs takes around 100 seconds .
Thus before influencing the signal , any deviation is amplified by which is exponential in the number of dimensions . For this reason the problem affects training of 3D CNNs more severely than conventional 2D systems . For countering it , we adopt the recently proposed Batch Normalisation ( BN ) technique to all hidden layers ( ) , which allows normalization of the FM activations at every optimization step in order to better preserve the signal .
document : 3D Face Morphable Models “ In - the - Wild ” 3D Morphable Models ( 3DMMs ) are powerful statistical models of 3D facial shape and texture , and among the state - of - the - art methods for reconstructing facial shape from single images . With the advent of new 3D sensors , many 3D facial datasets have been collected containing both neutral as well as expressive faces .
We present Face Swapping GAN ( FSGAN ) for face swapping and reenactment . Unlike previous work , FSGAN is subject agnostic and can be applied to pairs of faces without requiring training on those faces . To this end , we describe a number of technical contributions . We derive a novel recurrent neural network (RNN)-based approach for face reenactment which adjusts for both pose and expression variations and can be applied to a single image or a video sequence . For video sequences , we introduce continuous interpolation of the face views based on reenactment , Delaunay Triangulation , and barycentric coordinates .
Given the pose information , the first CNN predicts a dynamic texture map that contains time-coherent high-frequency details , and the second CNN conditions the generation of the final video on the temporally coherent output of the first CNN . We demonstrate several applications of our approach , such as human reenactment and novel view synthesis from monocular video , where we show significant improvement over the state of the art both qualitatively and quantitatively .
Given the pose information , the first CNN predicts a dynamic texture map that contains time-coherent high-frequency details , and the second CNN conditions the generation of the final video on the temporally coherent output of the first CNN . We demonstrate several applications of our approach , such as human reenactment and novel view synthesis from monocular video , where we show significant improvement over the state of the art both qualitatively and quantitatively .
In this work , we propose an encoder-decoder-like architecture to perform face reenact- ment in image sequences . Our goal is to transfer the training subject identity to a given test subject , regularazing the generation with Action units and gaze vectors to generate more lifelike results .
Animating a static face image with target facial expressions and movements is important in the area of image editing and movie production . This face reenactment process is challenging due to the complex geometry and movement of human faces . Previous work usually requires a large set of images from the same person to model the appearance .
Recent face reenactment studies have achieved remarkable success either between two identities or in the many-to-one task . However , existing methods have limited scalability when the target person is not a predefined specific identity .
Abstract This article summarizes the dissertation “ Face2Face : Realtime Facial Reenactment ” by Justus Thies ( Eurographics Graphics Dissertation Online , 2017 ) . It shows advances in the field of 3D reconstruction of human faces using commodity hardware .
g . , gender and expression , can not only provide a natural description of facial images , but also offer a unified viewpoint for understanding many facial animation and manipulation tasks . For example , the goal of facial avatar and reenactment is to transfer the facial expression attributes of a source actor to a target actor . In most applications such as expression transfer , accessory removal and age progression , the animation only modifies the related attribute without changing the identity .
The effectiveness of Mutual Information ( MI ) as a technique for automated multi-sensor image registration has previously been demonstrated for medical and remote sensing applications . In this paper , a new General Weighted MI ( GWMI ) approach that improves the robustness of MI to local maxima , particularly in the case of registering optical imagery and 3D point clouds , is presented . Two different methods including a Gaussian Mixture Model ( GMM ) and Kernel Density Estimation have been used to define the weight function of joint probability , regardless of the modality of the data being registered .
Analogous to image based convolutional architectures , recently introduced graph based architectures afford similar filtering and pooling operations on arbitrary graphs . We adopt these graph based methods to 3D point clouds to introduce a generic vector representation of 3D graphs , we call graph 3D ( G3D ) . We believe we are the first to use large scale transfer learning on 3D point cloud data and demonstrate the discriminant power of our salient latent representation of 3D point clouds on unforeseen test sets .
That is , if the captured depth image contains around 50 thousand points in the cloud for a single pose for one individual , then the storage size of the entire dataset will be in order of giga if not tera bytes . With these motivations , this work introduces a new technique for 3D point clouds generation using a neural modeling system to handle the differences caused by heterogeneous depth cameras , and to generate a new face canonical compact representation . The proposed system reduces the stored 3D dataset size , and if required , provides an accurate dataset regeneration .
First , recognition level of aerial object is obtained for task requirement analysis . Then a scheme of generating 3D point clouds is given to acquire data of 3D scannerless imaging LiDAR . By applying Zernike moment 's feature extraction algorithm and correlation coefficient method , the data features of the simulated point cloud is extracted and recognized to decide if the recognition results satisfy the task requirement .
Objects of different sizes can be modelled using image sequences from commercial digital cameras that are processed by web services and freely available software packages , forming low cost systems for numerous applications ( restoration , historical care of monuments , visualization , analysis of the state of construction and the damage , etc . ) . In this contribution various cultural objects ( historical buildings , statues/figures , archaeological finds , etc . ) have been reconstructed in order to investigate the potential of this technology which enables the automatic generation of 3D point clouds or surface models ( as 3D polygons ) with photo-realistic texture from image data . These so-called low cost systems represent an efficient alternative to expensive terrestrial laser scanning syst ...
The objective of the study is to assess the accuracy of 3D models generated by computer vision automated web-based applications ( such as Autodesk 123D Catch ) and compare the output to 3D models produced by optical technologies using multi-image photogrammetry and commercial 3D scanner . The web-based application enables the automatic generation of 3D point clouds or surface models ( as 3D polygons ) with photo-realistic texture from image data thus represent an efficient alternative to expensive terrestrial laser scanning systems for the as-built documentation of 3D objects in cultural heritage . The output of the study will be in the form of 3D replication of the Petalaindera , using 3D print technology .
However , this approach has limitations in the construction industry due to expensive and fragile equipments , lack of portability and need of trained operators . This study aims at investigating an alternate technology to generate as-built 3D point clouds using photos taken using handheld digital cameras , compare them against the original as-built 3D models , and check for accuracy of the modeling process . This analysis can aid in a more reliable and effective decision making due to its cost effectiveness and ease of use , particularly in heavy infrastructure projects which are continually undergoing rehabilitation work .
Abstract Over-segmentation , or super-pixel generation , is a common preliminary stage for many computer vision applications . New acquisition technologies enable the capturing of 3D point clouds that contain color and geometrical information . This 3D information can be utilized to improve the results of over-segmentation , which uses mainly color information , and to generate clusters of points we call super-points .
3D generative shape modeling is a fundamental research area in computer vision and interactive computer graphics , with many real-world applications . This paper investigates the novel problem of generating 3D shape point cloud geometry from a symbolic part tree representation . In order to learn such a conditional shape generation procedure in an end-to-end fashion , we propose a conditional GAN " part tree"-to-"point cloud " model ( PT2PC ) that disentangles the structural and geometric factors .
Abstract The article presents a robot 3-D scanning system for generation of 3-D point clouds of an object by using multi-view 3-D scanning and novel data registration . Our approach mainly comprises two important elements in the determination of next best probe pose and multiple-view point clouds registration .
Selection is a fundamental task in exploratory analysis and visualization of 3D point clouds . Prior researches on selection methods were developed mainly based on heuristics such as local point density , thus limiting their applicability in general data .
We can then easily locate the ego - lane boundaries using a few heuristic filters . Noting that lane boundaries on highways are usually marked with retro - reflective materials , we first filter out low - reflectivity surfaces such as asphalt in our 3D point cloud maps and only consider points with high enough laser return intensities . We then filter out other reflective surfaces such as cars and traffic signs by only considering points whose heights are close enough the ground plane .
Driven by this unorthodox output form and the inherent ambiguity in groundtruth , we design architecture , loss function and learning paradigm that are novel and effective . Our final solution is a conditional shape sampler , capable of predicting multiple plausible 3D point clouds from an input image . In experiments not only can our system outperform state - ofthe - art methods on single image based 3d reconstruction benchmarks ; but it also shows strong performance for 3d shape completion and promising ability in making multiple plausible predictions .
We tried octree based solution to increase the volume resolution , but found it still hard to recover surface level details as much as our model . PSG produces sparse 3D point clouds , and it is non - trivial to recover meshes from them . This is due to the applied Chamfer loss acting like a regression loss which gives too much degree of freedom to the point cloud .
DEVELOPMENT OF IMMERSIVE OUTDOOR AUGMENTED REALITY SYSTEM FOR LANDSCAPE SIMULATION Removal of Non-Gaussian Jitter Noise for Shape From Focus Through Improved Maximum Correntropy Criterion Kalman Filter An introduction to image-based 3D surface reconstruction and a survey of photometric stereo methods Learning Single-View 3D Reconstruction with Adversarial Training Understanding dynamic scenes Machine learning techniques for high dimensional data Processing of Hydroacoustic and LiDAR Data for Three-dimensional Surface Reconstruction 3D-GMNet: Learning to Estimate 3D Shape from A Single Image As A Gaussian Mixture Self-supervised Learning of 3D Objects from Natural Images Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression A Point Set Generation Network for 3D Object Reconstruction from a Single Image R3MR: Region Growing Based 3D Mesh Reconstruction for Big Data Platform How is “Internet of the 3D Printed Products” going to Affect Our Lives? Human gesture recognition via bag of angles for 3D virtual city planning in CAVE environment Game development tools for simulating robots and creating interactive learning experiences 3D City building model sharing service on the web Generalizing Spatial Transformers to Projective Geometry with Applications to 2D/3D Registration Digital photogrammetric station "Delta" and symmetric intensity-based stereo Robust and efficient change detection algorithm based on 3D line segments Automatic Multiorgan Segmentation Using Hierarchically Registered Probabilistic Atlases RGBD-Dog: Predicting Canine Pose from RGBD Sensors Learning a Hierarchical Latent-Variable Model of 3D Shapes PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation 3D Face Morphable Models "In-the-Wild" FSGAN: Subject Agnostic Face Swapping and Reenactment Neural Human Video Rendering by Learning Dynamic Textures and Rendering-to-Video Translation. Neural Human Video Rendering: Joint Learning of Dynamic Textures and Rendering-to-Video Translation Generative Video Face Reenactment by AUs and Gaze Regularization FaR-GAN for One-Shot Face Reenactment FaceSwapNet: Landmark Guided Many-to-Many Face Reenactment Face2Face: Real-time facial reenactment Deep Identity-aware Transfer of Facial Attributes Automatic Registration of Multi-Source Data Using Mutual Information General-Purpose Deep Point Cloud Feature Extractor Neural Generative Models for 3D Faces with Application in 3D Texture Free Face Recognition Rolling Simulation Method for Acquiring Pixel Parameter of Airborne 3D Imaging LiDAR Potential of Automatic 3D Object Reconstruction from Multiple Images for Applications in Architecture, Cultural Heritage and Archaeology Replication of the Petalaindera boat: a comparative study of optical sensors and computer vision technologies for digital heritage practice in Malaysia Evaluation of accuracy of as-built 3D modeling from photos taken by handheld digital cameras Graph Based Over-Segmentation Methods for 3D Point Clouds PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree Conditions A 3-D point clouds scanning and registration methodology for automatic object digitization LassoNet: Deep Lasso-Selection of 3D Point Clouds An Empirical Evaluation of Deep Learning on Highway Driving A Point Set Generation Network for 3D Object Reconstruction from a Single Image Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images
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2017 IEEE International Conference on Computer Vision (ICCV) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) MMSP Geoinformatics ICPR Cloud-Based Benchmarking of Medical Image Analysis 2018 International Conference on 3D Vision (3DV) Robotics: Science and Systems 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Comput. Medical Imaging Graph. Medical Image Anal. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv WACV ArXiv 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ECCV
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