The factors affecting the depth estimation in face region cause major accuracy problems . They are analyzed and possible improvements to enhance the 3D face-shape reconstruction are presented . Among the local algorithms relying on the fronto-parallel assumption , our algorithm is the best ranked algorithm . We also demonstrate that our algorithm is working well on practical examples as for disparity estimation of a tomato seedling and a 3D reconstruction of a face . Using 3D information in facial recognition can improve the performance of facial recognition over conventional 2D facial recognition . To perform 3D facial recognition , a 3D reconstruction of the face has to be available . Because most commercially available 3D scanning solutions that have a sufﬁcient accuracy for performing facial recognition are often expensive , it was decided to make an open-source 3D laser-line scanner that can be used for facial acquisition . Depending on only one image and one generic model , the computing cost of memory and time is largely reduced . The 3D face reconstructed can be easily deformed to form different expressions and mouth shapes . Experiments show that this approach is fast and efficient and has an output of a lifelike Chinese talking face . This paper presents a 3D face reconstruction face model by using PCA-based reconstruction model in synthesizing faces of individual . A 3D face reconstruction model is derived by transforming the shape and texture of the training sets into a vector space representation . We compare the performance of various feature detectors in terms of accuracy and robustness of the sparse and dense reconstructions . Our experimental results demonstrate the effectiveness of the proposed selection method from the observation that the chosen feature detector produces 3D reconstructed face models with superior accuracy and robustness to image noise . More recently , the state - of - the - art method of that produces the average ( neutral ) 3D face from a collection of personal photos , firstly performs landmark detection , then fits a 3DMM using a sparse set of points , then solves an optimization problem similar to the one in , then performs surface normal estimation as in and finally performs surface reconstruction by solving another energy minimisation problem . Simplifying the technical challenges involved in the aforementioned works is the main motivation of this paper . 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 Joint 3D Face Reconstruction and Dense Face Alignment from A Single Image with 2D - Assisted Self - Supervised Learning section : Figure 1 : Dense face alignment ( odd rows ) and 3D face reconstruction ( even rows ) results from our proposed method . For alignment , only 68 key points are plotted for clear display ; for 3D reconstruction , reconstructed shapes are rendered with head light for better view . Although some recent works bypass the 3DMM parameter regression and use image - to - volume [ reference ] or image - to - image [ reference ] strategy instead , the ground truths are all still needed and generated from 3DMM using 300W - LP , still lacking diversity . In order to overcome the intrinsic limitation of existing 3D face recovery models , we propose a novel learning method that leverages 2D " in - the - wild " face images to effectively supervise and facilitate the 3D face model learning . With the method , the trained 3D face model can perform 3D face reconstruction and dense face alignment well . 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 . Furthermore , we make the implementation of our algorithm publicly available , which we believe can be of great benefit to the community , given the lack of robust open - source implementations for fitting 3DMMs . Due to lack of ground - truth data , the majority of the 3D face reconstruction papers report only qualitative results . In this paper , in order to provide quantitative evaluations , we collected a new dataset of 3D facial surfaces , using Kinect Fusion , which has many “ in - the - wild ” characteristics , even though it is captured indoors . However , it is straight - forward to extend our method to incorporate global illumination algorithms and more complex backgrounds . section : 3D Shape Reconstruction from RGB Images Comparison to state - of - the - art We compare our work to 3D - R2N2 [ reference ] which is the state - of - the - art in deep learning based 3D object generation . In this paper , we propose a method for extracting more detailed physical knowledge from a set of RGB-D images taken from the same scene but from different views using qualitative reasoning and intuitive physical models . Rather than providing a simple contact relation graph and approximating stability over convex shapes , our method is able to provide a detailed supporting relation analysis based on a volumetric representation . Specifically , true supporting relations between objects ( e.g. , if an object supports another object by touching it on the side or if the object above contributes to the stability of the object below ) are identified . The 3D visualization of the blood vessel system is based on a ray-tracing algorithm in combination with Phong 's illumination model . The chosen volumetric object representation is particularly appropriate to realize clipping operations , a useful tool to analyze internal structures of the capillary networks . The algorithms for visualization and manipulation of three-dimensional objects ( e.g. , rotation , clipping , zooming , etc . ) in medical image data are integrated in the software system VolumeView , which has been developed at the Institut fur Medizinische Informatik . Geometric object representation can be manipulated intuitively in real-time but does not preserve interior information . Volumetric data representation , on the other hand , preserves volume content but direct manipulation is compute-intensive . 3-D texture mapping provides an alternative in representing volumetric information . Visually unimportant features such as tubes and holes may be eliminated from a model by the open and close morphological operators . Our simplification approach accepts polygonal models as input , scan converts these to create a volumetric description , performs topology modification , and then converts the results back to polygons . We then apply a topology-preserving polygon simplification technique to produce a final model . To solve this discrepancy , a compact data representation has to be generated and a trade-off between resource consumption and analytical precision has to be found . There are many existing volumetric representation generating methods , almost all of which adopts some kind of hand-engineered heuristics to extract the effective portion of the datasets . However , the trade-off between resource consumption and analytical quality could not be well established due to the introduction of hand-engineered heuristics . The goal of a NBV inspection system is to completely inspect an object in the fewest number of views . Our approach to complete this task is to study a volumetric representation of the model after each new view is obtained . Since the volumetric model consists of known and unknown information , we want to nd the viewpoint from which the largest amount of unknown data can be acquired . • We empirically validate the usefulness of the volumetric input and output representations by comparing the performance of each input type ( i.e. , 2D depth map and voxelized grid ) and output type ( i.e. , 3D coordinates and per - voxel likelihood ) . • Their model learns viewpoint - invariant features , which makes the model robust to viewpoint variations . Volumetric representation using depth information . Wu et al . We use four kinds of building blocks in designing the V2V - PoseNet . The first one is the volumetric basic block that consists of a volumetric convolution , volumetric batch normalization [ reference ] , and the activation function ( i.e. , ReLU ) . This block is located in the first and last parts of the network . Our CNN architecture for 3D segmentation is based on the “ hourglass network ” of an extension of the fully convolutional network of using skip connections and residual learning . Our volumetric architecture consists of two hourglass modules which are stacked together without intermediate supervision . The input is an RGB image and the output is a volume of of real values . To compute CD and EMD , their predicted and ground truth volumes are sampled by iterative farthest point sampling [ reference ] to a discrete set of points with the same cardinality as ours . We post - processed our point - set into a volumetric one with the same resolution as in 3D - R2N2 when computing IoU. Refer to Sec 5.9 for details . In Fig 7 we report the result of our network compared with the single - view 3D - R2N2 . document : Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation Precise 3D segmentation of infant brain tissues is an essential step towards comprehensive volumetric studies and quantitative analysis of early brain developement . However , computing such segmentations is very challenging , especially for 6 - month infant brain , due to the poor image quality , among other difficulties inherent to infant brain MRI , e.g. , the isointense contrast between white and gray matter and the severe partial volume effect due to small brain sizes . Stealth viewers enable users to view a simulation event as a passive ' stealth ' observer . The stealth viewer displays simulation entities with a 3D model representation in a 3D virtual world . The software allows basic features such as the ability to fly anywhere and look anywhere ; configure networks and maps ; connect to and disconnect from a network ; set user preferences ; entity search and attach behaviours . The " shape filter " has the overall form of the predicted 2D projection of the 3D model , while the cross-section of the filter is designed to collect the gradient responses along the shape . The 3D-model-based representation is designed to emphasize the changes in 2D object shape due to motion , while de-emphasizing the variations due to lighting and other imaging conditions . We have found that the set of sparse measurements using a relatively small number of particles is able to approximate the high-dimensional state distribution very effectively . However , these systems have been limited in their ability to visualize inter-related multiple data sets concurrently due to the fact that an adequate visual representation method had yet to be developed . This research introduces a 3D model-based visual representation method utilizing the power of information technology . The proposed representation enables the users to show a holistic picture of a project by applying the multiple project data sets to the geometric attributes ( such as shapes , faces , and edges ) of the 3D building model through color- tone variation and motion . Previous work has considered a pairwise data collection approach , in which pairs of 3D shapes are shown to human participants and they are asked to choose one from each pair that they perceive to be more aesthetic . In this research , we study the question of whether the 3D modeling representation ( e.g. polygon , points , or voxels ) affects how people perceive the aesthetics of shape pairs . We find surprising results : for example the single-view and multi-view of shape pairs lead to similar user aesthetics choices ; and a relatively low resolution of points or voxels is comparable to polygon meshes as they do not lead to significantly different user aesthetics choices . This is equivalent to training a ConvNet with two streams ( i.e. , Siamese Style ConvNets [ reference ] ) that takes in two local 3D patches and predicts whether or not they correspond to each other . section : 3D Data Representation For each interest point , we first extract a 3D volumetric representation for the local region surrounding it . These TDF values are truncated , normalized and then flipped to be between 1 ( on surface ) and 0 ( far from surface ) . This form of 3D representation is cross - compatible with 3D meshes , point - clouds , and depth maps . Analogous to 2D RGB pixel matrices for color images , 3D TDF voxel grids also provide a natural volumetric encoding of 3D space that is suitable as input to a 3D ConvNet . section : Closely related work This section reviews closely related work in 3D face reconstruction , depth estimation using CNNs and work on 3D representation modelling with CNNs . 3D face reconstruction . The method of is currently considered the state - of - the - art in 3DMM fitting . It is based on a single CNN that is iteratively applied to estimate the model parameters using as input the 2D image and a 3D - based representation produced at the previous iteration . Finally , a state - of - the - art cascaded regression landmark - based 3DMM fitting method is proposed in . As the table shows , converting the input representation type from the 2D depth map to 3D voxelized form ( also converting the model from 2D CNN to 3D CNN ) substantially improves performance , regardless of output representation . This justifies the effectiveness of the proposed 3D input representation that is free from perspective distortion . The results also show that converting the output representation from the 3D coordinates to the per - voxel likelihood increases the performance significantly , regardless of the input type . Given the success of SIFT , there have been several attempts at embedding rotational and translational invariance . Other features such as viewpoint invariant 3D feature maps , histograms of 3D joint locations , multifractal spectrum , volumetric attention models , and volumetric convolutional filters have been proposed for 3D modeling . Instead of proposing invariant features , Ozuysal et al . trained a classifier for each viewpoint . section : Conclusion In this paper , we proposed the Variational Shape Learner , a hierarchical latent - variable model for 3D shape modelling , learnable through variational inference . In particular , we have demonstrated 3D shape generation results on a popular benchmark , the ModelNet dataset . They provide a set of 2D filters , which when convolved with input data , transform the data to informative features for higher level inference . In this paper , we focus on the problem of learning a 3D shape representation by a deep neural network . We keep two goals in mind when designing the network : the shape features should be discriminative for shape recognition and efficient for extraction at runtime . However , the former lacks the relation of depth with plat and the latter leads to large computation . In this paper , we propose the Combined Heatmap and Coordinate Regression ( CHCR ) , which is an end-to-end method for 3D facial landmark detection from a single 2D image . To achieve that , we innovatively present the combined heatmap of three channels , and each channel of heatmap records the likelihoods of landmarks location of any two different axes . Existing approaches typically adopt a suboptimal two-step strategy , performing 2D landmark localization followed by depth estimation . In this paper , we propose the Joint Voxel and Coordinate Regression ( JVCR ) method for 3D facial landmark localization , addressing it more effectively in an end-to-end fashion . First , a compact volumetric representation is proposed to encode the per-voxel likelihood of positions being the 3D landmarks . The emergence of 3D data theoretically provides an alternative to overcome these weaknesses in the 2D domain . This article proposes a novel approach to 3D facial landmarking , which combines both the advantages of feature-based methods as well as model-based ones in a progressive three-stage coarse-to-fine manner ( initial , intermediate , and fine stages ) . For the initial stage , a few fiducial landmarks ( i.e. , the nose tip and two inner eye corners ) are robustly detected through curvature analysis , and these points are further exploited to initialize the subsequent stage . We present our graph matching approach for 3D facial feature localisation . The work here uses a basic graph model ( three vertices and three arcs ) to locate the inner eye corners and the nose tip simultaneously . On the other hand , posed smiles showed higher intensities in the lower part of the face . Furthermore , the 3D facial landmark technique revealed that the left eyebrow displayed stronger intensity during spontaneous smiles than the right eyebrow . These findings suggest a potential application of landmark based emotion recognition that spontaneous smiles can be distinguished from posed smiles via measuring relative intensities between the upper and lower face with a focus on left-sided asymmetry in the upper region . section : Introduction 3D face reconstruction is the problem of recovering the 3D facial geometry from 2D images . Despite many years of research , it is still an open problem in Vision and Graphics research . A simple but effective method to simulate particular human face realistically is presented . Provided with frontal and side facial images and a general 3D polygonal facial mesh with muscular vectors , facial feature lines are fitted by Snake automatically . Based on relative displacement between the particular person 's feature lines and the general model 's , we perform variational interpolation to fit the particular facial geometry . Among these challenges , the most significant is the difficulty to establish coherent and accurate correspondence among a set of images , especially when these images are captured under unconstrained in-the-wild condition . This work proposes a method , Deep Recurrent 3D FAce Reconstruction ( DRFAR ) , to solve the task of multi-view 3D face reconstruction using a subspace representation of the 3D facial shape and a deep recurrent neural network that consists of both a deep convolutional neural network ( DCNN ) and a recurrent neural network ( RNN ) . The DCNN disentangles the facial identity and the facial expression components for each single image independently , while the RNN fuses identity-related features from the DCNN and aggregates the identity specific contextual information , or the identity signal , from the whole set of images to estimate the facial identity parameter , which is robust to variations in image quality and is consistent over the whole set of images . Since there is limited in-the-wild 3D FER dataset , we first construct 3D facial data from available 2D dataset using recent advances in 3D face reconstruction . The 3D facial geometry representation is then extracted by deep learning technique . In addition , we also take advantage of manipulating the 3D face , such as using 2D projected images of 3D face as additional input for FER . Fast and robust 3D reconstruction of facial geometric structure from a single image is a challenging task with numerous applications , but there exist two problems when applied " in the wild " : the 3D estimates are unstable for different photos of the same subject ; the 3D estimates are over-regularized and generic . In response , a robust method for regressing discriminative 3D morphable face models(3DMM ) is described to support face recognition and 3D mask printing . In this work we propose a new method of 3D face computational photography based on a facial expressions training dataset composed of both facial range images ( 3D geometry ) and facial texture ( 2D photo ) . The method allows to obtain a 3D representation of facial geometry given only a 2D photo and a set of facial landmarks , which undergoes a series of transformations through the estimated texture and geometry spaces . In the training stage , principal component analysis is used to represent the face dataset , thus defining an orthonormal basis of texture and another of geometry . The following content introduced : the general face model and its data structure , using front , side photos and the general face model to establish 3D specific face models , editing facial models based on B-spline , and the system environment of displaying and editing is based on OpenGL . This is a simple and applied method of constructing 3D facial models by 2D photos , and a new facial model can be finished based on editing the key points of the old model . It can applied widely in film , game education and medicine etc . We evaluate our approach by testing on several benchmark datasets , including the Middlebury and Garg et al. datasets . In addition , we show application of our method for constructing 3D Morphable Facial Models from dynamic 3D data . In the proposed method , however , a large number of feature points and a whole facial image are not required in order to estimate facial expressions . The proposed method estimates the head pose and facial expression based on a simplified 3D deformable facial shape model approximated by a cylindrical model . In order to show the effectiveness of the proposed method , we performed facial expression estimation experiments . The new LBP technique is applied to shape index of 3D facial surface data . The novelty of this technique illustrates that a modified local binary pattern termed as Triangular Local Binary Pattern ( TR-LBP ) , which gives new texture representation of 3D facial surface for improvement of facial texture classification performance compared to other variants of LBP . In this paper , authors have also described the TR-LBP technique by extending it on 2D intensity image of same subjects . Our CNN works with just a single 2D facial image , does not require accurate alignment nor establishes dense correspondence between images , works for arbitrary facial poses and expressions , and can be used to reconstruct the whole 3D facial geometry ( including the non-visible parts of the face ) bypassing the construction ( during training ) and fitting ( during testing ) of a 3D Morphable Model . We achieve this via a simple CNN architecture that performs direct regression of a volumetric representation of the 3D facial geometry from a single 2D image . We also demonstrate how the related task of facial landmark localization can be incorporated into the proposed framework and help improve reconstruction quality , especially for the cases of large poses and facial expressions . More recently , works are proposed to use CNN directly obtain the reconstructed 3D face bypassing the 3DMM coefficients regression . In [ reference ] , Jackson et al . propose to map the image pixels to a volumetric representation of the 3D facial geometry through CNN - based regression . While their method is not restricted to the 3DMM space any more , it needs a complex network structure and a lot of time to predict the voxel information . 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 . In the following paragraph , we provide examples from 5 predominant approaches : In the 3D Morphable Model ( 3DMM ) , the most popular approach for estimating the full 3D facial structure from a single image ( among others ) , training includes an iterative flow procedure for dense image correspondence which is prone to failure . Additionally , testing requires a careful initialisation for solving a difficult highly non - convex optimization problem , which is slow . It does not estimate 3DMM parameters and , in fact , it completely bypasses the fitting of a 3DMM . Instead , our method directly produces a 3D volumetric representation of the facial geometry . Because of this fundamental difference , our method is also radically different in terms of the CNN architecture used : we used one that is able to make spatial predictions at a voxel level , as opposed to the networks of which holistically predict the 3DMM parameters . Given that the error of state - of - the - art methods is of the order of a few mms , we conclude that discretization by produces negligible error . Given our volumetric facial representation , the problem of regressing the 3D coordinates of all vertices of a facial scan is reduced to one of 3D binary volume segmentation . We approach this problem using recent CNN architectures from semantic image segmentation and their extensions , as described in the next subsection . Parametric models based on linear regression and Kriging were developed to correlate the mesh point coordinates of a face model to a set of feature parameters . These models efficiently generate 3D facial geometry approximating to individual users . A design software tool implementing Free Form Deformation ( FFD ) was introduced to adjust the frame design interactively and to enable real-time design evaluation . This paper presents a simple yet effective approach for 3D face recognition . A novel 3D facial surface representation , namely Multi-Scale Local Binary Pattern ( MS-LBP ) Depth Map , is proposed , which is used along with the Shape Index ( SI ) Map to increase the distinctiveness of smooth range faces . Scale Invariant Feature Transform ( SIFT ) is introduced to extract local features to enhance the robustness to pose variations . This is primarily attributed to the fact that the community has made a considerable effort to collect and annotate facial images captured under unconstrained conditions ( commonly referred to as “ in - the - wild ” ) and to the discriminative methodologies that can capitalise on the availability of such large amount of data . Nevertheless , discriminative techniques can not be applied for 3D facial shape estimation “ in - the - wild ” , due to lack of ground - truth data . 3D facial shape estimation from single images has attracted the attention of many researchers the past twenty years . We show that we are able to recover shapes with more detail than is possible using purely landmark - driven approaches . Our newly introduced “ in - the - wild ” KinectFusion dataset allows for the first time a quantitative evaluation of 3D facial reconstruction techniques in the wild , and on these evaluations we demonstrate that our in the wild formulation is state of the art , outperforming classical 3DMM approaches by a considerable margin . bibliography : References Following [ reference ] , we use 40 basis from the Basel Face Model ( BFM ) [ reference ] to generate the face shape component and 10 basis from the Face Warehouse dataset [ reference ] to generate the facial expression component . The rendering of a 3D face shape is thus formulated as : where S ∈ R 3N is the mean shape , 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 : Previous results has shown that this can be achieved automatically using surface-based Active Appearance Models ( AAMs ) of the bones . In this paper we describe a method of refining the segmentations and correspondences by building a volumetric appearance model using the minimum message length principle . We present results from a study of 12 subjects which show that the new approach achieves a significant improvement in segmentation accuracy compared to the surface AAM approach , and reduce the variance in cartilage thickness measurements for key regions of interest . 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 . We adopt the 3D morphable model ( 3DMM ) [ reference ] to recover the 3D facial geometry from a single face image . The 3DMM renders 3D face shape S ∈ R 3N that stores 3D coordinates of N mesh vertices with linear combination over a set of PCA basis . Our method uses only 2D images as input to the proposed CNN architecture . 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 .
The factors affecting the depth estimation in face region cause major accuracy problems . They are analyzed and possible improvements to enhance the 3D face-shape reconstruction are presented .
Among the local algorithms relying on the fronto-parallel assumption , our algorithm is the best ranked algorithm . We also demonstrate that our algorithm is working well on practical examples as for disparity estimation of a tomato seedling and a 3D reconstruction of a face .
Using 3D information in facial recognition can improve the performance of facial recognition over conventional 2D facial recognition . To perform 3D facial recognition , a 3D reconstruction of the face has to be available . Because most commercially available 3D scanning solutions that have a sufﬁcient accuracy for performing facial recognition are often expensive , it was decided to make an open-source 3D laser-line scanner that can be used for facial acquisition .
Depending on only one image and one generic model , the computing cost of memory and time is largely reduced . The 3D face reconstructed can be easily deformed to form different expressions and mouth shapes . Experiments show that this approach is fast and efficient and has an output of a lifelike Chinese talking face .
This paper presents a 3D face reconstruction face model by using PCA-based reconstruction model in synthesizing faces of individual . A 3D face reconstruction model is derived by transforming the shape and texture of the training sets into a vector space representation .
We compare the performance of various feature detectors in terms of accuracy and robustness of the sparse and dense reconstructions . Our experimental results demonstrate the effectiveness of the proposed selection method from the observation that the chosen feature detector produces 3D reconstructed face models with superior accuracy and robustness to image noise .
More recently , the state - of - the - art method of that produces the average ( neutral ) 3D face from a collection of personal photos , firstly performs landmark detection , then fits a 3DMM using a sparse set of points , then solves an optimization problem similar to the one in , then performs surface normal estimation as in and finally performs surface reconstruction by solving another energy minimisation problem . Simplifying the technical challenges involved in the aforementioned works is the main motivation of this paper .
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
Joint 3D Face Reconstruction and Dense Face Alignment from A Single Image with 2D - Assisted Self - Supervised Learning section : Figure 1 : Dense face alignment ( odd rows ) and 3D face reconstruction ( even rows ) results from our proposed method . For alignment , only 68 key points are plotted for clear display ; for 3D reconstruction , reconstructed shapes are rendered with head light for better view .
Although some recent works bypass the 3DMM parameter regression and use image - to - volume [ reference ] or image - to - image [ reference ] strategy instead , the ground truths are all still needed and generated from 3DMM using 300W - LP , still lacking diversity . In order to overcome the intrinsic limitation of existing 3D face recovery models , we propose a novel learning method that leverages 2D " in - the - wild " face images to effectively supervise and facilitate the 3D face model learning . With the method , the trained 3D face model can perform 3D face reconstruction and dense face alignment well .
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 .
Furthermore , we make the implementation of our algorithm publicly available , which we believe can be of great benefit to the community , given the lack of robust open - source implementations for fitting 3DMMs . Due to lack of ground - truth data , the majority of the 3D face reconstruction papers report only qualitative results . In this paper , in order to provide quantitative evaluations , we collected a new dataset of 3D facial surfaces , using Kinect Fusion , which has many “ in - the - wild ” characteristics , even though it is captured indoors .
However , it is straight - forward to extend our method to incorporate global illumination algorithms and more complex backgrounds . section : 3D Shape Reconstruction from RGB Images Comparison to state - of - the - art We compare our work to 3D - R2N2 [ reference ] which is the state - of - the - art in deep learning based 3D object generation .
In this paper , we propose a method for extracting more detailed physical knowledge from a set of RGB-D images taken from the same scene but from different views using qualitative reasoning and intuitive physical models . Rather than providing a simple contact relation graph and approximating stability over convex shapes , our method is able to provide a detailed supporting relation analysis based on a volumetric representation . Specifically , true supporting relations between objects ( e.g. , if an object supports another object by touching it on the side or if the object above contributes to the stability of the object below ) are identified .
The 3D visualization of the blood vessel system is based on a ray-tracing algorithm in combination with Phong 's illumination model . The chosen volumetric object representation is particularly appropriate to realize clipping operations , a useful tool to analyze internal structures of the capillary networks . The algorithms for visualization and manipulation of three-dimensional objects ( e.g. , rotation , clipping , zooming , etc . ) in medical image data are integrated in the software system VolumeView , which has been developed at the Institut fur Medizinische Informatik .
Geometric object representation can be manipulated intuitively in real-time but does not preserve interior information . Volumetric data representation , on the other hand , preserves volume content but direct manipulation is compute-intensive . 3-D texture mapping provides an alternative in representing volumetric information .
Visually unimportant features such as tubes and holes may be eliminated from a model by the open and close morphological operators . Our simplification approach accepts polygonal models as input , scan converts these to create a volumetric description , performs topology modification , and then converts the results back to polygons . We then apply a topology-preserving polygon simplification technique to produce a final model .
To solve this discrepancy , a compact data representation has to be generated and a trade-off between resource consumption and analytical precision has to be found . There are many existing volumetric representation generating methods , almost all of which adopts some kind of hand-engineered heuristics to extract the effective portion of the datasets . However , the trade-off between resource consumption and analytical quality could not be well established due to the introduction of hand-engineered heuristics .
The goal of a NBV inspection system is to completely inspect an object in the fewest number of views . Our approach to complete this task is to study a volumetric representation of the model after each new view is obtained . Since the volumetric model consists of known and unknown information , we want to nd the viewpoint from which the largest amount of unknown data can be acquired .
• We empirically validate the usefulness of the volumetric input and output representations by comparing the performance of each input type ( i.e. , 2D depth map and voxelized grid ) and output type ( i.e. , 3D coordinates and per - voxel likelihood ) . •
Their model learns viewpoint - invariant features , which makes the model robust to viewpoint variations . Volumetric representation using depth information . Wu et al .
We use four kinds of building blocks in designing the V2V - PoseNet . The first one is the volumetric basic block that consists of a volumetric convolution , volumetric batch normalization [ reference ] , and the activation function ( i.e. , ReLU ) . This block is located in the first and last parts of the network .
Our CNN architecture for 3D segmentation is based on the “ hourglass network ” of an extension of the fully convolutional network of using skip connections and residual learning . Our volumetric architecture consists of two hourglass modules which are stacked together without intermediate supervision . The input is an RGB image and the output is a volume of of real values .
To compute CD and EMD , their predicted and ground truth volumes are sampled by iterative farthest point sampling [ reference ] to a discrete set of points with the same cardinality as ours . We post - processed our point - set into a volumetric one with the same resolution as in 3D - R2N2 when computing IoU. Refer to Sec 5.9 for details . In Fig 7 we report the result of our network compared with the single - view 3D - R2N2 .
document : Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation Precise 3D segmentation of infant brain tissues is an essential step towards comprehensive volumetric studies and quantitative analysis of early brain developement . However , computing such segmentations is very challenging , especially for 6 - month infant brain , due to the poor image quality , among other difficulties inherent to infant brain MRI , e.g. , the isointense contrast between white and gray matter and the severe partial volume effect due to small brain sizes .
Stealth viewers enable users to view a simulation event as a passive ' stealth ' observer . The stealth viewer displays simulation entities with a 3D model representation in a 3D virtual world . The software allows basic features such as the ability to fly anywhere and look anywhere ; configure networks and maps ; connect to and disconnect from a network ; set user preferences ; entity search and attach behaviours .
The " shape filter " has the overall form of the predicted 2D projection of the 3D model , while the cross-section of the filter is designed to collect the gradient responses along the shape . The 3D-model-based representation is designed to emphasize the changes in 2D object shape due to motion , while de-emphasizing the variations due to lighting and other imaging conditions . We have found that the set of sparse measurements using a relatively small number of particles is able to approximate the high-dimensional state distribution very effectively .
However , these systems have been limited in their ability to visualize inter-related multiple data sets concurrently due to the fact that an adequate visual representation method had yet to be developed . This research introduces a 3D model-based visual representation method utilizing the power of information technology . The proposed representation enables the users to show a holistic picture of a project by applying the multiple project data sets to the geometric attributes ( such as shapes , faces , and edges ) of the 3D building model through color- tone variation and motion .
Previous work has considered a pairwise data collection approach , in which pairs of 3D shapes are shown to human participants and they are asked to choose one from each pair that they perceive to be more aesthetic . In this research , we study the question of whether the 3D modeling representation ( e.g. polygon , points , or voxels ) affects how people perceive the aesthetics of shape pairs . We find surprising results : for example the single-view and multi-view of shape pairs lead to similar user aesthetics choices ; and a relatively low resolution of points or voxels is comparable to polygon meshes as they do not lead to significantly different user aesthetics choices .
This is equivalent to training a ConvNet with two streams ( i.e. , Siamese Style ConvNets [ reference ] ) that takes in two local 3D patches and predicts whether or not they correspond to each other . section : 3D Data Representation For each interest point , we first extract a 3D volumetric representation for the local region surrounding it .
These TDF values are truncated , normalized and then flipped to be between 1 ( on surface ) and 0 ( far from surface ) . This form of 3D representation is cross - compatible with 3D meshes , point - clouds , and depth maps . Analogous to 2D RGB pixel matrices for color images , 3D TDF voxel grids also provide a natural volumetric encoding of 3D space that is suitable as input to a 3D ConvNet .
section : Closely related work This section reviews closely related work in 3D face reconstruction , depth estimation using CNNs and work on 3D representation modelling with CNNs . 3D face reconstruction .
The method of is currently considered the state - of - the - art in 3DMM fitting . It is based on a single CNN that is iteratively applied to estimate the model parameters using as input the 2D image and a 3D - based representation produced at the previous iteration . Finally , a state - of - the - art cascaded regression landmark - based 3DMM fitting method is proposed in .
As the table shows , converting the input representation type from the 2D depth map to 3D voxelized form ( also converting the model from 2D CNN to 3D CNN ) substantially improves performance , regardless of output representation . This justifies the effectiveness of the proposed 3D input representation that is free from perspective distortion . The results also show that converting the output representation from the 3D coordinates to the per - voxel likelihood increases the performance significantly , regardless of the input type .
Given the success of SIFT , there have been several attempts at embedding rotational and translational invariance . Other features such as viewpoint invariant 3D feature maps , histograms of 3D joint locations , multifractal spectrum , volumetric attention models , and volumetric convolutional filters have been proposed for 3D modeling . Instead of proposing invariant features , Ozuysal et al . trained a classifier for each viewpoint .
section : Conclusion In this paper , we proposed the Variational Shape Learner , a hierarchical latent - variable model for 3D shape modelling , learnable through variational inference . In particular , we have demonstrated 3D shape generation results on a popular benchmark , the ModelNet dataset .
They provide a set of 2D filters , which when convolved with input data , transform the data to informative features for higher level inference . In this paper , we focus on the problem of learning a 3D shape representation by a deep neural network . We keep two goals in mind when designing the network : the shape features should be discriminative for shape recognition and efficient for extraction at runtime .
However , the former lacks the relation of depth with plat and the latter leads to large computation . In this paper , we propose the Combined Heatmap and Coordinate Regression ( CHCR ) , which is an end-to-end method for 3D facial landmark detection from a single 2D image . To achieve that , we innovatively present the combined heatmap of three channels , and each channel of heatmap records the likelihoods of landmarks location of any two different axes .
Existing approaches typically adopt a suboptimal two-step strategy , performing 2D landmark localization followed by depth estimation . In this paper , we propose the Joint Voxel and Coordinate Regression ( JVCR ) method for 3D facial landmark localization , addressing it more effectively in an end-to-end fashion . First , a compact volumetric representation is proposed to encode the per-voxel likelihood of positions being the 3D landmarks .
The emergence of 3D data theoretically provides an alternative to overcome these weaknesses in the 2D domain . This article proposes a novel approach to 3D facial landmarking , which combines both the advantages of feature-based methods as well as model-based ones in a progressive three-stage coarse-to-fine manner ( initial , intermediate , and fine stages ) . For the initial stage , a few fiducial landmarks ( i.e. , the nose tip and two inner eye corners ) are robustly detected through curvature analysis , and these points are further exploited to initialize the subsequent stage .
We present our graph matching approach for 3D facial feature localisation . The work here uses a basic graph model ( three vertices and three arcs ) to locate the inner eye corners and the nose tip simultaneously .
On the other hand , posed smiles showed higher intensities in the lower part of the face . Furthermore , the 3D facial landmark technique revealed that the left eyebrow displayed stronger intensity during spontaneous smiles than the right eyebrow . These findings suggest a potential application of landmark based emotion recognition that spontaneous smiles can be distinguished from posed smiles via measuring relative intensities between the upper and lower face with a focus on left-sided asymmetry in the upper region .
section : Introduction 3D face reconstruction is the problem of recovering the 3D facial geometry from 2D images . Despite many years of research , it is still an open problem in Vision and Graphics research .
A simple but effective method to simulate particular human face realistically is presented . Provided with frontal and side facial images and a general 3D polygonal facial mesh with muscular vectors , facial feature lines are fitted by Snake automatically . Based on relative displacement between the particular person 's feature lines and the general model 's , we perform variational interpolation to fit the particular facial geometry .
Among these challenges , the most significant is the difficulty to establish coherent and accurate correspondence among a set of images , especially when these images are captured under unconstrained in-the-wild condition . This work proposes a method , Deep Recurrent 3D FAce Reconstruction ( DRFAR ) , to solve the task of multi-view 3D face reconstruction using a subspace representation of the 3D facial shape and a deep recurrent neural network that consists of both a deep convolutional neural network ( DCNN ) and a recurrent neural network ( RNN ) . The DCNN disentangles the facial identity and the facial expression components for each single image independently , while the RNN fuses identity-related features from the DCNN and aggregates the identity specific contextual information , or the identity signal , from the whole set of images to estimate the facial identity parameter , which is robust to variations in image quality and is consistent over the whole set of images .
Since there is limited in-the-wild 3D FER dataset , we first construct 3D facial data from available 2D dataset using recent advances in 3D face reconstruction . The 3D facial geometry representation is then extracted by deep learning technique . In addition , we also take advantage of manipulating the 3D face , such as using 2D projected images of 3D face as additional input for FER .
Fast and robust 3D reconstruction of facial geometric structure from a single image is a challenging task with numerous applications , but there exist two problems when applied " in the wild " : the 3D estimates are unstable for different photos of the same subject ; the 3D estimates are over-regularized and generic . In response , a robust method for regressing discriminative 3D morphable face models(3DMM ) is described to support face recognition and 3D mask printing .
In this work we propose a new method of 3D face computational photography based on a facial expressions training dataset composed of both facial range images ( 3D geometry ) and facial texture ( 2D photo ) . The method allows to obtain a 3D representation of facial geometry given only a 2D photo and a set of facial landmarks , which undergoes a series of transformations through the estimated texture and geometry spaces . In the training stage , principal component analysis is used to represent the face dataset , thus defining an orthonormal basis of texture and another of geometry .
The following content introduced : the general face model and its data structure , using front , side photos and the general face model to establish 3D specific face models , editing facial models based on B-spline , and the system environment of displaying and editing is based on OpenGL . This is a simple and applied method of constructing 3D facial models by 2D photos , and a new facial model can be finished based on editing the key points of the old model . It can applied widely in film , game education and medicine etc .
We evaluate our approach by testing on several benchmark datasets , including the Middlebury and Garg et al. datasets . In addition , we show application of our method for constructing 3D Morphable Facial Models from dynamic 3D data .
In the proposed method , however , a large number of feature points and a whole facial image are not required in order to estimate facial expressions . The proposed method estimates the head pose and facial expression based on a simplified 3D deformable facial shape model approximated by a cylindrical model . In order to show the effectiveness of the proposed method , we performed facial expression estimation experiments .
The new LBP technique is applied to shape index of 3D facial surface data . The novelty of this technique illustrates that a modified local binary pattern termed as Triangular Local Binary Pattern ( TR-LBP ) , which gives new texture representation of 3D facial surface for improvement of facial texture classification performance compared to other variants of LBP . In this paper , authors have also described the TR-LBP technique by extending it on 2D intensity image of same subjects .
Our CNN works with just a single 2D facial image , does not require accurate alignment nor establishes dense correspondence between images , works for arbitrary facial poses and expressions , and can be used to reconstruct the whole 3D facial geometry ( including the non-visible parts of the face ) bypassing the construction ( during training ) and fitting ( during testing ) of a 3D Morphable Model . We achieve this via a simple CNN architecture that performs direct regression of a volumetric representation of the 3D facial geometry from a single 2D image . We also demonstrate how the related task of facial landmark localization can be incorporated into the proposed framework and help improve reconstruction quality , especially for the cases of large poses and facial expressions .
More recently , works are proposed to use CNN directly obtain the reconstructed 3D face bypassing the 3DMM coefficients regression . In [ reference ] , Jackson et al . propose to map the image pixels to a volumetric representation of the 3D facial geometry through CNN - based regression . While their method is not restricted to the 3DMM space any more , it needs a complex network structure and a lot of time to predict the voxel information .
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 .
In the following paragraph , we provide examples from 5 predominant approaches : In the 3D Morphable Model ( 3DMM ) , the most popular approach for estimating the full 3D facial structure from a single image ( among others ) , training includes an iterative flow procedure for dense image correspondence which is prone to failure . Additionally , testing requires a careful initialisation for solving a difficult highly non - convex optimization problem , which is slow .
It does not estimate 3DMM parameters and , in fact , it completely bypasses the fitting of a 3DMM . Instead , our method directly produces a 3D volumetric representation of the facial geometry . Because of this fundamental difference , our method is also radically different in terms of the CNN architecture used : we used one that is able to make spatial predictions at a voxel level , as opposed to the networks of which holistically predict the 3DMM parameters .
Given that the error of state - of - the - art methods is of the order of a few mms , we conclude that discretization by produces negligible error . Given our volumetric facial representation , the problem of regressing the 3D coordinates of all vertices of a facial scan is reduced to one of 3D binary volume segmentation . We approach this problem using recent CNN architectures from semantic image segmentation and their extensions , as described in the next subsection .
Parametric models based on linear regression and Kriging were developed to correlate the mesh point coordinates of a face model to a set of feature parameters . These models efficiently generate 3D facial geometry approximating to individual users . A design software tool implementing Free Form Deformation ( FFD ) was introduced to adjust the frame design interactively and to enable real-time design evaluation .
This paper presents a simple yet effective approach for 3D face recognition . A novel 3D facial surface representation , namely Multi-Scale Local Binary Pattern ( MS-LBP ) Depth Map , is proposed , which is used along with the Shape Index ( SI ) Map to increase the distinctiveness of smooth range faces . Scale Invariant Feature Transform ( SIFT ) is introduced to extract local features to enhance the robustness to pose variations .
This is primarily attributed to the fact that the community has made a considerable effort to collect and annotate facial images captured under unconstrained conditions ( commonly referred to as “ in - the - wild ” ) and to the discriminative methodologies that can capitalise on the availability of such large amount of data . Nevertheless , discriminative techniques can not be applied for 3D facial shape estimation “ in - the - wild ” , due to lack of ground - truth data . 3D facial shape estimation from single images has attracted the attention of many researchers the past twenty years .
We show that we are able to recover shapes with more detail than is possible using purely landmark - driven approaches . Our newly introduced “ in - the - wild ” KinectFusion dataset allows for the first time a quantitative evaluation of 3D facial reconstruction techniques in the wild , and on these evaluations we demonstrate that our in the wild formulation is state of the art , outperforming classical 3DMM approaches by a considerable margin . bibliography : References
Following [ reference ] , we use 40 basis from the Basel Face Model ( BFM ) [ reference ] to generate the face shape component and 10 basis from the Face Warehouse dataset [ reference ] to generate the facial expression component . The rendering of a 3D face shape is thus formulated as : where S ∈ R 3N is the mean shape ,
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 :
Previous results has shown that this can be achieved automatically using surface-based Active Appearance Models ( AAMs ) of the bones . In this paper we describe a method of refining the segmentations and correspondences by building a volumetric appearance model using the minimum message length principle . We present results from a study of 12 subjects which show that the new approach achieves a significant improvement in segmentation accuracy compared to the surface AAM approach , and reduce the variance in cartilage thickness measurements for key regions of interest .
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 .
We adopt the 3D morphable model ( 3DMM ) [ reference ] to recover the 3D facial geometry from a single face image . The 3DMM renders 3D face shape S ∈ R 3N that stores 3D coordinates of N mesh vertices with linear combination over a set of PCA basis .
Our method uses only 2D images as input to the proposed CNN architecture . 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 .
Towards Reconstructing a 3D Face Model from an Uncontrolled Video Sequence Local Stereo Matching Using Adaptive Local Segmentation A low cost 3D laser-line scanner for facial acquisitio 3D reconstruction from a single image for a Chinese talking face Reconstruction of 3D Faces Using Face Space Coefficient, Texture Space and Shape Space Automated Scene-Specific Selection of Feature Detectors for 3D Face Reconstruction Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression 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" A Point Set Generation Network for 3D Object Reconstruction from a Single Image Support Relation Analysis for Objects in Multiple View RGB-D Images 3D visualization of microvascular blood vessel networks Dynamic volume texture mapping and model deformation for visually realistic surgical simulation. Simplification and Repair of Polygonal Models Using Volumetric Techniques Adaptive Deep Learning based Time-Varying Volume Compression Determining Optimal Sensor Poses in 3-D Object Inspection V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map 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 Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation ADFStealthViewer User Manual 3D Shape-Encoded Particle Filter for Object Tracking and Its Application to Human Body Tracking PROJECT DASHBOARD: CONCURRENT VISUAL REPRESENTATION METHOD OF PROJECT METRICS ON 3D BUILDING MODELS Polygons, points, or voxels?: stimuli selection for crowdsourcing aesthetics preferences of 3D shape pairs 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map Towards Viewpoint Invariant 3D Human Pose Estimation Learning a Hierarchical Latent-Variable Model of 3D Shapes FPNN: Field Probing Neural Networks for 3D Data A Light-Weighted Network for Facial Landmark Detection via Combined Heatmap and Coordinate Regression Joint Voxel and Coordinate Regression for Accurate 3D Facial Landmark Localization Expression Robust 3D Facial Landmarking via Progressive Coarse-to-Fine Tuning 3D Facial Landmark Localisation by Matching Simple Descriptors Differences in Facial Expressions between Spontaneous and Posed Smiles: Automated Method by Action Units and Three-Dimensional Facial Landmarks Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression Quick Customization of Particular Human Face and Muscle-Driven Expression Animation Multi-view 3D face reconstruction with deep recurrent neural networks A 3d Face Modeling Approach for in-The-Wild Facial Expression Recognition on Image Datasets 3D Face Reconstruction Based on Convolutional Neural Network 3D human face reconstruction using principal components spaces Facial Constructing System Based on Photos and General Face Model Video interpolation using optical flow and Laplacian smoothness Facial expression estimation using simplified head model based on particle filtering TR-LBP: A modified Local Binary Pattem-based technique for 3D face recognition Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression 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" Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression Parametric modeling of 3D human faces using anthropometric data 3D Face recognition using distinctiveness enhanced facial representations and local feature hybrid matching 3D Face Morphable Models "In-the-Wild" Joint 3D Face Reconstruction and Dense Face Alignment from A Single Image with 2D-Assisted Self-Supervised Learning Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee MRI 3D Face Morphable Models "In-the-Wild" Joint 3D Face Reconstruction and Dense Face Alignment from A Single Image with 2D-Assisted Self-Supervised Learning Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression
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CW IVIC (2) ISVC (1) 2017 IEEE International Conference on Computer Vision (ICCV) ArXiv 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) BigData 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE International Conference on Computer Vision (ICCV) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Comput. Medical Imaging Graph. CAe@Expressive 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE International Conference on Computer Vision (ICCV) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ECCV 2018 International Conference on 3D Vision (3DV) NIPS ICME 2017 IEEE International Conference on Computer Vision (ICCV) ICIP AMC ICCV ArXiv 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE International Conference on Computer Vision (ICCV) IEEM BTAS 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv ISBI 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv 2017 IEEE International Conference on Computer Vision (ICCV)
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