We transfer the template model labeled with the meaningful part to the scanned face model to find the corresponding part of each meaningful part of the template model . This technique can be used to the several applications such as 3D face modeling , facial animation , virtual facial surgery and 3D printing . This paper introduce a new way to build the individuated 3D human body modeling by using two 2D photos to reconstruct 3D Mannequins which including 3D human body modeling and 3D human face modeling . Our work mainly concentrates in extracting human body feature points from 2D photos by the theory of anthropometry and converting these feature points to deformation parameters which will be applied to the initial 3D model of the human body . These approaches are extensively studied , and both approaches have limitations . We introduce a semi-automatic method for 3D facial appearance modeling from video that addresses previous problems . Four main novelties are proposed : ( 1 ) a 3D generative facial appearance model integrates both structure and appearance , ( 2 ) the model is learned in a semi-unsupervised manner from video sequences , greatly reducing the need for tedious manual pre-processing , ( 3 ) a constrained flow-based stochastic sampling technique improves specificity in the learning process , and ( 4 ) in the appearance learning step , we automatically select the most representative images from the sequence . In recent years , it has become the most active research focus both at home and broad . 3D modeling of face is the key step to realize face recognition , and the technique of face 3D modeling has obtained extensive applications in many fields , such as film , animation , interactive games , video conference , human-computer interaction , reverse engineering , medical and public safety . In this paper , the technology of face 3D modeling based on projective rectification is presented and the reconstruction of face 3D digital model can be achieved by it . 3D modeling of human face has been an active research topic in computer vision and computer graphics communities . In this paper we develop a face-modeling system from calibrated stereo video input . In this paper , facial expressions of the three Turkish presidential candidates Demirtas , Erdogan and Ihsanoglu ( in alphabetical order ) are analyzed during the publicity speeches featured at TRT ( Turkish Radio and Television ) on 03.08.2014 . FaceReader is used for the analysis where 3D modeling of the face is achieved using the active appearance models ( AAM ) . Over 500 landmark points are tracked and analyzed for obtaining the facial expressions during the whole speech . is the expression principle basis and α exp ∈ R 10 denotes the corresponding expression coefficient . The target of singleimage based 3D face modeling is to predict the coefficients α exp and α s for 3D face rendering from a single 2D image . After obtaining the 3D face shape S , it can be projected onto the 2D image plane with the scale orthographic projection to generate a 2D face from specified viewpoint : Nevertheless , even thought the results of are quite interesting , given that there is no prior of the facial surface , the methods only recover 2.5D representations of the faces and particular smooth approximations of the facial normals . 3D facial shape recovery from a single image under “ in - the - wild ” conditions is still an open and challenging problem in computer vision mainly due to the fact that : The general problem of extracting the 3D facial shape from a single image is an ill - posed problem which is notoriously difficult to be solved without the use of any statistical priors for the shape and texture of faces . That is , without prior knowledge regarding the shape of the object at - hand there are inherent ambiguities present in the problem . For 3D point cloud registration , Go-ICP [ Yang et al. , 2016 ] has been shown to obtain the global optimal solution for a pair composed of a model point cloud and a data point cloud . Go-ICP mostly has been investigated only on standard sets of point clouds . OSAC can handle the challenging cases of matching highly self-similar models . Based on the proposed LFSH and OSAC , a coarse-to-fine algorithm can be formed for 3D point cloud registration . Experiments and comparisons with the state-of-the-art descriptors demonstrate that LFSH is highly discriminative , robust , and significantly faster than other descriptors . 3D point-cloud recognition with PointNet and its variants has received remarkable progress . A missing ingredient , however , is the ability to automatically evaluate point-wise importance w.r.t.\ ! Motivated by recent successes on learning 3D feature representations , we present a Siamese network to generate representative 3D descriptors for 3D point matching in point cloud registration . Our system , dubbed HAF-Net , consists of feature extraction module , hierarchical feature reweighting and recalibration module ( HRR ) , as well as feature aggregation and compression module . The hand is lighted by an 850 nm light emitting diode ( LED ) light source , binocular images for 3D reconstruction are obtained by the stereo cameras . The hand vein′s spatial structure is described by hand veins feature , an optimized kernel correlation analysis approach is proposed for 3D point cloud matching . Experimental results of 200 different point clouds data show the proposed system is feasible and effective , the recognition rate is 98 % , false rejection rate is 2 % and the false accept rate is 0 % , the feature′ s dimension is ranged from 8000 to 12000 , which is higher than that of scale invariant feature transform ( SIFT ) . The relationship between the acquired 3D point cloud data and the originally designed CAD data can be obtained by projective transformation . If the projective transformation matrix between the two is obtained , 3D point cloud data registration can be performed . In order to calculate 3D projective transformation matrix , corresponding points between 3D point cloud data and CAD data are required . In contrast to existing adaptation works that conduct domain confusion learning on semantic features to remove domain-specific factors , CDN aligns different domain distributions by modulating the semantic features of one domain conditioned on the learned domain-vector of another domain . Extensive experiments show that CDN outperforms existing methods remarkably on both real-to-real and synthetic-to-real adaptation benchmarks , including 2D image detection and 3D point cloud detection . Since the early ' 80s , when Digital Photogrammetry was in its infancy , important progress has been made in the area of automations using the most common photogrammetric procedures . The main objective of this paper is the full automation of 3D Point Cloud Registration process . In particular , an alternative registration method is being presented , based on corresponding points , which are detected on overlapping images that come with each scan using a structured light scanner . The proposed registration method is composed of two steps . The first step consists of US 3D points cloud alignment with the 3D bone model . Then , the second step performs the widely-known Iterative Closest Point ( ICP ) algorithm . In general , the field of how to predict geometries in an end - to - end fashion is quite a virgin land . In particular , our output , 3D point set , is still not a typical object in the deep learning community . A point set contains orderless samples from a metric - measure space . Such method can be applied to face augmentation by leveraging facial expression changes . The technique is able to perform both 3D facial modeling and facial motion tracking without the need of pre-scanning or training for a specific user . The proposed approach builds on an existing method that we refer as FaceCap , which uses a blendshape representation and a Bump image for tracking facial motion and capturing geometric details . Third , the course will explore the latest in 3D gesture recognition performance in terms of accuracy and gesture set size and discuss how different applications are making use of 3D gestural interaction . Finally , the course will present a summary of important future research directions that are critical to improving 3D gestural interaction . In this paper , we examine the state of the field of 3D gestural interfaces by presenting the latest strategies on how to collect the raw 3D gesture data from the user and how to accurately analyze this raw data to correctly recognize 3D gestures users perform . In addition , we examine the latest in 3D gesture recognition performance in terms of accuracy and gesture set size and discuss how different applications are making use of 3D gestural interaction . Finally , we present ideas for future research in this thriving and active research area . This contribution is dedicated to present tomographic investigations on 3D vegetation imaging for future spaceborne SAR missions . The main problem to tackle when performing tomography via repeat-pass spaceborne data is that the temporal decorrelation between acquisitions can be very severe making it difficult to achieve reliable results . ZHANG , WEI . Multimodal Pedagogical Planning for 3D Learning Environments A Unified Framework . ( Under the Direction of Dr. James C. Lester and Dr. R. Michael Young . ) The fourth edition of SEG ’s best seller is a valuable , comprehensive reference that is a must for every geophysicist , geologist , explorationist , engineer , energy adviser , economist , editor , and student involved in the field . Hundreds of terms have been added since publication of the third edition in 1991 , reflecting rapid evolution of the science , especially in the areas of engineering and production problems , 3D ( including multicomponent ) acquisition and processing , visualization , S- and converted waves , interpretation , anisotropy , AVO , geostatistics , geohazards , neural networks , tomography , downhole measurements , horizontal drilling , and deepwater work . Definitions of hundreds of other terms have been updated . This model was then 3D printed with polyamide through a commercial online service . Two different methods are discussed for the key segmentation and 3D reconstruction steps , by either using professional commercial software , i.e. , Materialise Mimics , or utilizing a combination of the widely available software Adobe Photoshop , as well as a freeware software , OsiriX Lite . The model or scheme gives a reasonable standard which decides what extent the 3D model is simplified . This scheme can be used in the 3D object-recognition and the robot vision . In this paper , 3D model is the convex model . We show that our self-supervised learning approach can substantially boost the accuracies of 3D deep learning networks on the volumetric medical datasets without using extra data . To our best knowledge , this is the first work focusing on the self-supervised learning of 3D neural networks . Previous methods addressing this problem relied on both color and depth ( RGB-D ) images to learn low-dimensional viewpoint descriptors for object pose retrieval . In the proposed method , a novel quaternion-based multi-objective loss function is used , which combines manifold learning and regression to learn 3D pose descriptors and direct 3D object pose estimation , using only color ( RGB ) images . The 3D object pose can then be obtained either by using the learned descriptors in the nearest neighbor ( NN ) search or by direct neural network regression . Keywords . 3D Computer graphics , animation , motion capture , compression , wavelet , motion graphs , motion bundles , structure extraction , animation synthesis Manual delineation practices require anatomical knowledge , are expensive , time consuming and can be inaccurate due to human error . Here , we describe a semantic segmentation network for tumor subregion segmentation from 3D MRIs based on encoder - decoder architecture . Due to a limited training dataset size , a variational auto - encoder branch is added to reconstruct the input image itself in order to regularize the shared decoder and impose additional constraints on its layers . section : Introduction As we try to duplicate the successes of current deep convolutional architectures in the 3D domain , we face a fundamental representational issue . Extant deep net architectures for both discriminative and generative learning in the signal domain are well suited to data that is regularly sampled , such as images , audio , or video . Fig 8 visualizes examples of the predictions . section : 3D Shape Completion from RGBD Images The neural network successfully guesses the missing parts of the model . paragraph : Deep network based methods With the success of deep networks , many have designed networks that can be trained end - to - end to predict 3D poses from images directly . Li et al . and Park et al . designed CNNs to jointly predict 2D and 3D poses . Recently , great progress has been made on 2D image understanding tasks , such as object detection and instance segmentation . However , beyond getting 2D bounding boxes or pixel masks , 3D understanding is eagerly in demand in many applications such as autonomous driving and augmented reality ( AR ) . With the popularity of 3D sensors deployed on mobile devices and autonomous vehicles , more and more 3D data is captured and processed . Considering the recent achievements in 3D face reconstruction which can construct a 3D face from 2D landmarks , we assume that a 3D model can be accurately fitted if sufficient 2D landmarks are provided . Therefore 3D evaluation can be degraded to 2D evaluation which also makes it possible to compare 3DDFA with other 2D face alignment methods . However , AFLW is not suitable for evaluating this task since only visible landmarks lead to serious ambiguity in 3D shape , as reflected by the fake good alignment phenomenon in Fig .
We transfer the template model labeled with the meaningful part to the scanned face model to find the corresponding part of each meaningful part of the template model . This technique can be used to the several applications such as 3D face modeling , facial animation , virtual facial surgery and 3D printing .
This paper introduce a new way to build the individuated 3D human body modeling by using two 2D photos to reconstruct 3D Mannequins which including 3D human body modeling and 3D human face modeling . Our work mainly concentrates in extracting human body feature points from 2D photos by the theory of anthropometry and converting these feature points to deformation parameters which will be applied to the initial 3D model of the human body .
These approaches are extensively studied , and both approaches have limitations . We introduce a semi-automatic method for 3D facial appearance modeling from video that addresses previous problems . Four main novelties are proposed : ( 1 ) a 3D generative facial appearance model integrates both structure and appearance , ( 2 ) the model is learned in a semi-unsupervised manner from video sequences , greatly reducing the need for tedious manual pre-processing , ( 3 ) a constrained flow-based stochastic sampling technique improves specificity in the learning process , and ( 4 ) in the appearance learning step , we automatically select the most representative images from the sequence .
In recent years , it has become the most active research focus both at home and broad . 3D modeling of face is the key step to realize face recognition , and the technique of face 3D modeling has obtained extensive applications in many fields , such as film , animation , interactive games , video conference , human-computer interaction , reverse engineering , medical and public safety . In this paper , the technology of face 3D modeling based on projective rectification is presented and the reconstruction of face 3D digital model can be achieved by it .
3D modeling of human face has been an active research topic in computer vision and computer graphics communities . In this paper we develop a face-modeling system from calibrated stereo video input .
In this paper , facial expressions of the three Turkish presidential candidates Demirtas , Erdogan and Ihsanoglu ( in alphabetical order ) are analyzed during the publicity speeches featured at TRT ( Turkish Radio and Television ) on 03.08.2014 . FaceReader is used for the analysis where 3D modeling of the face is achieved using the active appearance models ( AAM ) . Over 500 landmark points are tracked and analyzed for obtaining the facial expressions during the whole speech .
is the expression principle basis and α exp ∈ R 10 denotes the corresponding expression coefficient . The target of singleimage based 3D face modeling is to predict the coefficients α exp and α s for 3D face rendering from a single 2D image . After obtaining the 3D face shape S , it can be projected onto the 2D image plane with the scale orthographic projection to generate a 2D face from specified viewpoint :
Nevertheless , even thought the results of are quite interesting , given that there is no prior of the facial surface , the methods only recover 2.5D representations of the faces and particular smooth approximations of the facial normals . 3D facial shape recovery from a single image under “ in - the - wild ” conditions is still an open and challenging problem in computer vision mainly due to the fact that : The general problem of extracting the 3D facial shape from a single image is an ill - posed problem which is notoriously difficult to be solved without the use of any statistical priors for the shape and texture of faces . That is , without prior knowledge regarding the shape of the object at - hand there are inherent ambiguities present in the problem .
For 3D point cloud registration , Go-ICP [ Yang et al. , 2016 ] has been shown to obtain the global optimal solution for a pair composed of a model point cloud and a data point cloud . Go-ICP mostly has been investigated only on standard sets of point clouds .
OSAC can handle the challenging cases of matching highly self-similar models . Based on the proposed LFSH and OSAC , a coarse-to-fine algorithm can be formed for 3D point cloud registration . Experiments and comparisons with the state-of-the-art descriptors demonstrate that LFSH is highly discriminative , robust , and significantly faster than other descriptors .
3D point-cloud recognition with PointNet and its variants has received remarkable progress . A missing ingredient , however , is the ability to automatically evaluate point-wise importance w.r.t.\ !
Motivated by recent successes on learning 3D feature representations , we present a Siamese network to generate representative 3D descriptors for 3D point matching in point cloud registration . Our system , dubbed HAF-Net , consists of feature extraction module , hierarchical feature reweighting and recalibration module ( HRR ) , as well as feature aggregation and compression module .
The hand is lighted by an 850 nm light emitting diode ( LED ) light source , binocular images for 3D reconstruction are obtained by the stereo cameras . The hand vein′s spatial structure is described by hand veins feature , an optimized kernel correlation analysis approach is proposed for 3D point cloud matching . Experimental results of 200 different point clouds data show the proposed system is feasible and effective , the recognition rate is 98 % , false rejection rate is 2 % and the false accept rate is 0 % , the feature′ s dimension is ranged from 8000 to 12000 , which is higher than that of scale invariant feature transform ( SIFT ) .
The relationship between the acquired 3D point cloud data and the originally designed CAD data can be obtained by projective transformation . If the projective transformation matrix between the two is obtained , 3D point cloud data registration can be performed . In order to calculate 3D projective transformation matrix , corresponding points between 3D point cloud data and CAD data are required .
In contrast to existing adaptation works that conduct domain confusion learning on semantic features to remove domain-specific factors , CDN aligns different domain distributions by modulating the semantic features of one domain conditioned on the learned domain-vector of another domain . Extensive experiments show that CDN outperforms existing methods remarkably on both real-to-real and synthetic-to-real adaptation benchmarks , including 2D image detection and 3D point cloud detection .
Since the early ' 80s , when Digital Photogrammetry was in its infancy , important progress has been made in the area of automations using the most common photogrammetric procedures . The main objective of this paper is the full automation of 3D Point Cloud Registration process . In particular , an alternative registration method is being presented , based on corresponding points , which are detected on overlapping images that come with each scan using a structured light scanner .
The proposed registration method is composed of two steps . The first step consists of US 3D points cloud alignment with the 3D bone model . Then , the second step performs the widely-known Iterative Closest Point ( ICP ) algorithm .
In general , the field of how to predict geometries in an end - to - end fashion is quite a virgin land . In particular , our output , 3D point set , is still not a typical object in the deep learning community . A point set contains orderless samples from a metric - measure space .
Such method can be applied to face augmentation by leveraging facial expression changes . The technique is able to perform both 3D facial modeling and facial motion tracking without the need of pre-scanning or training for a specific user . The proposed approach builds on an existing method that we refer as FaceCap , which uses a blendshape representation and a Bump image for tracking facial motion and capturing geometric details .
Third , the course will explore the latest in 3D gesture recognition performance in terms of accuracy and gesture set size and discuss how different applications are making use of 3D gestural interaction . Finally , the course will present a summary of important future research directions that are critical to improving 3D gestural interaction .
In this paper , we examine the state of the field of 3D gestural interfaces by presenting the latest strategies on how to collect the raw 3D gesture data from the user and how to accurately analyze this raw data to correctly recognize 3D gestures users perform . In addition , we examine the latest in 3D gesture recognition performance in terms of accuracy and gesture set size and discuss how different applications are making use of 3D gestural interaction . Finally , we present ideas for future research in this thriving and active research area .
This contribution is dedicated to present tomographic investigations on 3D vegetation imaging for future spaceborne SAR missions . The main problem to tackle when performing tomography via repeat-pass spaceborne data is that the temporal decorrelation between acquisitions can be very severe making it difficult to achieve reliable results .
ZHANG , WEI . Multimodal Pedagogical Planning for 3D Learning Environments A Unified Framework . ( Under the Direction of Dr. James C. Lester and Dr. R. Michael Young . )
The fourth edition of SEG ’s best seller is a valuable , comprehensive reference that is a must for every geophysicist , geologist , explorationist , engineer , energy adviser , economist , editor , and student involved in the field . Hundreds of terms have been added since publication of the third edition in 1991 , reflecting rapid evolution of the science , especially in the areas of engineering and production problems , 3D ( including multicomponent ) acquisition and processing , visualization , S- and converted waves , interpretation , anisotropy , AVO , geostatistics , geohazards , neural networks , tomography , downhole measurements , horizontal drilling , and deepwater work . Definitions of hundreds of other terms have been updated .
This model was then 3D printed with polyamide through a commercial online service . Two different methods are discussed for the key segmentation and 3D reconstruction steps , by either using professional commercial software , i.e. , Materialise Mimics , or utilizing a combination of the widely available software Adobe Photoshop , as well as a freeware software , OsiriX Lite .
The model or scheme gives a reasonable standard which decides what extent the 3D model is simplified . This scheme can be used in the 3D object-recognition and the robot vision . In this paper , 3D model is the convex model .
We show that our self-supervised learning approach can substantially boost the accuracies of 3D deep learning networks on the volumetric medical datasets without using extra data . To our best knowledge , this is the first work focusing on the self-supervised learning of 3D neural networks .
Previous methods addressing this problem relied on both color and depth ( RGB-D ) images to learn low-dimensional viewpoint descriptors for object pose retrieval . In the proposed method , a novel quaternion-based multi-objective loss function is used , which combines manifold learning and regression to learn 3D pose descriptors and direct 3D object pose estimation , using only color ( RGB ) images . The 3D object pose can then be obtained either by using the learned descriptors in the nearest neighbor ( NN ) search or by direct neural network regression .
Keywords . 3D Computer graphics , animation , motion capture , compression , wavelet , motion graphs , motion bundles , structure extraction , animation synthesis
Manual delineation practices require anatomical knowledge , are expensive , time consuming and can be inaccurate due to human error . Here , we describe a semantic segmentation network for tumor subregion segmentation from 3D MRIs based on encoder - decoder architecture . Due to a limited training dataset size , a variational auto - encoder branch is added to reconstruct the input image itself in order to regularize the shared decoder and impose additional constraints on its layers .
section : Introduction As we try to duplicate the successes of current deep convolutional architectures in the 3D domain , we face a fundamental representational issue . Extant deep net architectures for both discriminative and generative learning in the signal domain are well suited to data that is regularly sampled , such as images , audio , or video .
Fig 8 visualizes examples of the predictions . section : 3D Shape Completion from RGBD Images The neural network successfully guesses the missing parts of the model .
paragraph : Deep network based methods With the success of deep networks , many have designed networks that can be trained end - to - end to predict 3D poses from images directly . Li et al . and Park et al . designed CNNs to jointly predict 2D and 3D poses .
Recently , great progress has been made on 2D image understanding tasks , such as object detection and instance segmentation . However , beyond getting 2D bounding boxes or pixel masks , 3D understanding is eagerly in demand in many applications such as autonomous driving and augmented reality ( AR ) . With the popularity of 3D sensors deployed on mobile devices and autonomous vehicles , more and more 3D data is captured and processed .
Considering the recent achievements in 3D face reconstruction which can construct a 3D face from 2D landmarks , we assume that a 3D model can be accurately fitted if sufficient 2D landmarks are provided . Therefore 3D evaluation can be degraded to 2D evaluation which also makes it possible to compare 3DDFA with other 2D face alignment methods . However , AFLW is not suitable for evaluating this task since only visible landmarks lead to serious ambiguity in 3D shape , as reflected by the fake good alignment phenomenon in Fig .
Automatic 3D face component analysis technique Reconstruction of 3D mannequin by two-dimensional photos Learning 3D appearance models from video Face 3D Modeling Based on Projective Rectification Three-dimensional animated face modeling from stereo video Turkish Presidential Elections TRT Publicity Speech Facial Expression Analysis Joint 3D Face Reconstruction and Dense Face Alignment from A Single Image with 2D-Assisted Self-Supervised Learning 3D Face Morphable Models "In-the-Wild" Alignment of Point Clouds for Comparison of Infrastructures in Civil Engineering on Quality Control in Metrology A fast and robust local descriptor for 3D point cloud registration PointCloud Saliency Maps A Hierarchical Attention Fused Descriptor for 3D Point Matching Hand Vein Recognition Based on Three Dimensional Point Clouds Matching Registration of point cloud data for HDD stamped base inspection Adapting Object Detectors with Conditional Domain Normalization Point Cloud Registration Based on Image Correspondences FULL AUTOMATIC IMAGE-TO-PATIENT REGISTRATION OF MRI AND ULTRASOUND IMAGES IN COMPUTER-ASSISTED HUMERUS SURGERY A Point Set Generation Network for 3D Object Reconstruction from a Single Image Real-Time Facial Motion Capture Using RGB-D Images Under Complex Motion and Occlusions An introduction to 3D gestural interfaces 3D Gestural Interaction: The State of the Field Spaceborne demonstration of coherent SAR tomography for future companion satellite SAR missions Multimodal Pedagogical Planning for 3D Learning Environments - A Unified Framework Encyclopedic dictionary of applied geophysics Nerves of Steel: a Low-Cost Method for 3D Printing the Cranial Nerves Polygon Mesh Simplification Based on Visual Resolution Principle Self-supervised Feature Learning for 3D Medical Images by Playing a Rubik's Cube 3D Object Pose Estimation Using Multi-Objective Quaternion Learning Compression de donnees d'animation acquises par capture de mouvements 3D MRI brain tumor segmentation using autoencoder regularization A Point Set Generation Network for 3D Object Reconstruction from a Single Image Exploiting Temporal Information for 3D Human Pose Estimation Frustum PointNets for 3D Object Detection from RGB-D Data Face Alignment Across Large Poses: A 3D Solution
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ICCE FGR VCIP ArXiv 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) SVR SIGGRAPH Courses IGARSS BrainLes@MICCAI 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ECCV 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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