The inverse of the forward model of image formation is used to propagate the gradient guidance from the low-resolution ( LR ) images to the HR image space . The gradient fields over multiple scales were exploited for more accurate edge localization in the reconstruction . The proposed SRR allows inter-volume motion during the MRI scans and can incorporate with the LR images with arbitrary orientations and displacements in the frequency space , such as orthogonal and origin-shifted scans . HAADF-STEM data is increasingly being used in the physical sciences to study materials in 3D because it is free from the diffraction effects seen in Bright Field STEM data and satisfies the projection requirement for tomography . Typically , reconstruction is performed using Filtered Back Projection ( FBP ) or the SIRT algorithm . In this paper , we develop a Bayesian reconstruction algorithm for HAADF-STEM tomography which models the image formation , the noise characteristics of the measurement , and the inherent smoothness in the object . The current release includes 3390 enzymes , 5645 compounds and 5207 reactions . The database is indispensable for the reconstruction of metabolic pathways in the completely sequenced organisms . The LIGAND database can be accessed through the WWW ( http://www.genome.ad.jp/dbget/ligand.html ) or may be downloaded by anonymous FTP ( ftp://kegg.genome.ad.jp/molecules/ligand/ ) . In this paper , we propose a wavelet-based-interpolation algorithm , which improves the effect of Fourier image reconstruction for 2 D synthetic aperture radiometer . The core idea is to get the VF ( Visibility Function ) data on grid points by using the interpolation algorithm . A method to greatly simplify the high-frame-rate ( HFR ) imaging system using a rotation of coordinates in image reconstruction was developed . A theory of Fourier image reconstruction was also developed and both in vitro ( on an ATS539 tissue-mimicking phantom ) and in vivo ( on a human heart ) experiments were performed to verify the theory . In this context , we describe in detail an empirical and theoretical study carried out on the relation between the double-bounce effect of buildings and their orientation angle . Then , a novel approach to the automatic detection and reconstruction of building radar footprints from VHR SAR images is pre-sented . Unlike most of the methods presented in the literature , the developed method can extract and reconstruct building radar footprints from single VHR SAR images . The development of methods for the automatic detection and reconstruction of building radar footprints fromsingle very high resolution ( VHR ) synthetic aperture radar ( SAR ) images is a difficult task for two main reasons : i ) the very high complexity of VHR SAR images ; and ii ) the need to develop efficient algorithms that can be applied to large images in order to be used in real applications . In this paper we present a novel method for automatic building detection , which also reconstructs the 2D radar footprint of the detected buildings . Methods for joint activity reconstruction and attenuation reconstruction of time-of-flight ( TOF ) PET data provide an effective solution to attenuation correction when no ( or incomplete or inaccurate ) information on attenuation is available . One of the main barriers limiting use of these methods in clinical practice is their lack of validation in a relatively large patient database . In order to allow real-time exploration of even the highest resolution data sets , adaptive techniques benefiting from the hierarchical nature of multi-resolution representations have gained special attention . In this paper we propose an adaptive approach to the fast reconstruction of iso-surfaces from regular volume data at arbitrary levels of detail . The algorithm has been designed to enable real-time navigation through complex structures while providing user-adjustable resolution levels . 2.5D map is a convenient and efficient approach to exploiting a massive three-dimensional ( 3D ) city model in web GIS . With the rapid development of oblique airborne photogrammetry and photo-based 3D reconstruction , 3D city models are becoming more and more accessible . 3D Geographic Information System ( GIS ) can support the interactive visualization of massive 3D city models on various platforms and devices . PURPOSE CT image reconstruction requires accurate knowledge of the used geometry or image quality might be degraded by misalignment artifacts . To overcome this issue , an intrinsic method , that is , a method not requiring a dedicated calibration phantom , to perform a raw data-based misalignment correction for CT is proposed herein that does not require redundant data and hence is applicable to measurements with less than 180 ∘ plus fan-angle of data . This tracks the dynamic joint configuration during walking . Direct experimental evaluation was provided by step length computation as well as complete motion reconstruction . This method has demonstrated stable long term tracking of walking and yields greater than 95 % accuracy for step length estimation . A new scheme for 3D reconstruction of implicit surfaces from large scattered point sets based on the radial basis functions ( RBFs ) is proposed in this paper . The partition of unity ( POU ) method and a binary tree is used to organize the point sets into some overlapping local subdomains and reconstructing a local surface for each of the subdomains from non-disjunct subsets of the points , we use only a single point at the offset of the surface to avoid the trivial solution of RBF linear system . The output of the decoder is added ( via a operation , defined in the sequel ) to a cumulative canvas matrix , which is ultimately used to reconstruct the image . The total number of time - steps consumed by the network before performing the reconstruction is a free parameter that must be specified in advance . For each image presented to the network , are initialised to learned biases , and the DRAW network iteratively computes the following equations for : where is the error image , is the concatenation of vectors and into a single vector , and denotes the logistic sigmoid function : . Feature maps after each recursion are used to reconstruct the target high - resolution image ( HR ) . Reconstruction method ( layers dedicated to reconstruction ) is the same for all recursions . As each recursion leads to a different HR prediction , we combine all predictions resulting from different levels of recursions to deliver a more accurate final prediction . The fact that these scores are much worse than the simple templated model indicates that further research is needed into better copying alone for content selection and better long term content ordering models . Test results are consistent with development results , indicating that the Conditional Copy model is most effective at BLEU , RG , and CS , and that reconstruction is quite helpful for improving the joint model . section : Human Evaluation This work explores the challenges facing neural data - to - document generation by introducing a new dataset , and proposing various metrics for automatically evaluating content selection , generation , and ordering . We see that recent ideas in copying and reconstruction lead to improvements on this task , but that there is a significant gap even between these neural models and templated systems . We hope to motivate researchers to focus further on generation problems that are relevant both to content selection and surface realization , but may not be reflected clearly in the model 's perplexity . In our first experiment , the task is to register pre - scanned object models to RGB - D scanning data for the Shelf & Tote benchmark in the Amazon Picking Challenge ( APC ) setting [ reference ] , as illustrated in Fig . 7 . This scenario is different from scene level reconstruction in the following two aspects : ( 1 ) object sizes and their geometric features are much smaller in scale and ( 2 ) the alignment here is from full pre - scanned models to partial scan data , instead of partial scans to partial scans . section : Method Yang is a method of Yang et al . [ reference ] that employs sparse coding and sparse dictionaries for learning a compact representation of the LR - HR priors / training samples and for sharp HR reconstruction results . Zeyde is a method of Zeyde et al . Other approaches , such as , train a top - down deconvolutional network to reconstruct the input image from a feature representation by minimizing the Euclidean reconstruction error in image space . However , there is inherent uncertainty in the reconstruction process , since the representations in higher layers of the DNN are trained to be invariant to irrelevant transformations and to ignore low - level details . With Euclidean training objective , the deconvolutional network tends to produce blurry images . Since the videos in the Human3.6 M dataset are captured by stationary cameras , the subjects in the dataset do not rotate that much to provide alternative views for NRSFM algorithm to perform well . Another reason is that human pose reconstruction is a specialized problem in which constraints from human body structure apply . paragraph : Data pre - processing Hypothesis 1 : The Denoising AE produces latent representations which are invariant to noise because it facilitates the reconstruction of de - noised images . We will demonstrate that this training strategy actually enforces invariance not only against noise but against a variety of different input augmentations . One principled approach to unsupervised learning is generative probabilistic modeling . Not only do generative probabilistic models have the ability to create novel content , they also have a wide range of reconstruction related applications including inpainting theis2015generative , oord2016pixel , DBLP : conf / icml / Sohl - DicksteinW15 , denoising balle2015density , colorization zhang2016colorful , and super - resolution bruna2015super . As data of interest are generally high - dimensional and highly structured , the challenge in this domain is building models that are powerful enough to capture its complexity yet still trainable . We present a multisensory method for estimating the transformation of a mobile phone between two images taken from its camera . Pose estimation is a necessary step for applications such as 3D reconstruction and panorama construction , but detecting and matching robust features can be computationally expensive . In this paper we propose a method for combining the inertial sensors ( accelerometers and gyroscopes ) of a mobile phone with its camera to provide a fast and accurate pose estimation . 3D Human Face Reconstruction or 3D Human Face Modeling is a construction of 3D geometry graphics of human face model which has been a very challenging research in both computer vision and computer graphics for over the last three decades . Researchers have proposed many methods for 3D human face reconstruction by using or not using 3D scanner . Registration of human cloud points is a key step in 3D human reconstruction . So , this paper proposes using depth camera to acquire human point cloud . We propose a novel end-to-end deep learning framework , capable of 3D human shape reconstruction from a 2D image without the need of a 3D prior parametric model . We employ a “ prior-less ” representation of the human shape using unordered point clouds . A method for obtaining point data by means of analysis of the position relations of view items , and an algorithm for pretreating input projection view data are presented in this paper . Good result are obtained when the algorithm is used in 3D solid reconstruction . It is essential to generate efficient three-dimensional ( 3D ) spatial data model in 3D modeling , because the data processed is multiple in scale and large in volume . Based on investigation some existed strategies of 3D solid reconstruction and the features of section plane views , the technology of 3D entity model reconstruction from section plane views based on ES is given in this paper . From the view of engineering application , this paper introduces the reconstruction model of ES and the expression way for the frame knowledge and mainly discusses the discretization of the 3D model face and reasoning algorithm based on discretization meshwork model . The evaluation shows that when the method is used with traditional feature detector and descriptor , it increases the number of detected keypoints , provides higher repeatability and improves the localization accuracy . We also demonstrate that such features will lead to more accurate and complete reconstructions when used in the application of 3D visual reconstruction . The MA3HO workshop is aimed at taking a leap forward in emerging research of multimedia access of 3D human objects , merging researchers in 3D graphics , 3D object recognition and retrieval , Multimedia , with attention to application fields where humans are highly significant , such as security , surveillance and biometry , animation and entertainment , video retrieval , sport analytics , natural interaction , cultural heritage , augmented and virtual reality and world wide web . Main subjects addressed are among the others : 3D human objects reconstruction from 2D views 3D pose estimation from 2D information 2D to 3D human object matching 3D human object categorization 3D people identification and re-identification 3D object/face similarity matching , indexing , and mining Feature extraction for 3D model segmentation Feature extraction for 3D motion detection and behavior classification 3D shape descriptors Retrieval with large distributed and heterogeneous 3D datasets and benchmarking Semantics-driven 3D object retrieval and classification 3D natural interfaces and search modalities The workshop has attracted 18 good quality submissions fairly distributed among different countries : China , Japan , Canada , USA , France , Italy and Germany . Many of the key arguments of the workshop call were addressed . Further on , we show that a simplified meshing can be applied to the patch group borders which enables a GPU centric real-time implementation . The presented algorithm is tested on real world test shoot data for the case of 3D reconstruction of humans . Then , we describe how we exploit the 3D information to select harder matching pairs and hard non - matching pairs with larger variability . subsection : BoW and 3D reconstruction The retrieval engine used in the work of Schonberger et al . builds upon BoW with fast spatial verification . Due to the fact that the information contained in one single image is not sufficient for 3D shape reconstruction , the existing model can not reconstruct 3D models very well . To tackle this problem , we propose a novel model which effectively utilizes the category information of objects to improve the performance of network on single view 3D reconstruction . Our model consists of two parts : rough shape generation network ( RSGN ) and category comparison network ( CCN ) . The latent space formed by the Seq2Seq encoder encodes both part structure and fine part geometry . The decoder can be adapted to perform several generative tasks including shape autoencoding , interpolation , novel shape generation , and single-view 3D reconstruction , where the generated shapes are all composed of meaningful parts . Existing works on single-image 3D reconstruction mainly focus on shape recovery . In this work , we study a new problem , that is , simultaneously recovering 3D shape and surface color from a single image , namely " colorful 3D reconstruction " . From a single view , humans are able to hallucinate the full 3D shape of the object in the image , even if it is from a novel , unseen category . Contemporary AI systems for single-image 3D reconstruction often lack this ability , because the shape priors they learned is often tied to the training object classes . In this paper , we study the task of single-image 3D reconstruction , but attempting to recover the full 3D shape of an object outside the training categories . Color encoded structured light is an important means for single-shot 3D reconstruction . In this article , we present a pseudorandom array-based structured light method . Single-view 3D shape reconstruction is an important but challenging problem , mainly for two reasons . First , as shape annotation is very expensive to acquire , current methods rely on synthetic data , in which ground-truth 3D annotation is easy to obtain . We present a new algorithm for single camera 3D reconstruction , or 3D input for human-computer interfaces , based on precise tracking of an elongated object , such as a pen , having a pattern of colored bands . To configure the system , the user provides no more than one labelled image of a handmade pointer , measurements of its colored bands , and the camera 's pinhole projection matrix . This paper introduces single-image 3D scene reconstruction from water reflection photography , i.e. , images capturing direct and water-reflected real-world scenes . Water reflection offers an additional viewpoint to the direct sight , collectively forming a stereo pair . The theories and techniques of 3D Reconstruction are one of the most important problems in the computer vision , Single- image-based 3D Reconstruction because of lacking enough several informations but hard attain to expect the result . This paper aims at the symmetry characteristic of most objects , putting forward make use of to imagine the symmetry flat surface according to Projection Inverse Transformation principle , handing over from the artificial to specify with each other to have already known that three picture that rightnesses are a little bit symmetries sit the mark and spaces to sit to mark the Projection Inverse Transformation matrix , Inverse the 3D position that the object surface characteristic order , then make use of the software of UG to carry out 3D models . At the heart of our approach is a small number of grammar rules that can describe the most common geometric structures , e.g. , two straights lines being co-linear or orthogonal , or that a line lying on a planar region etc . With these grammar rules , we re-frame single-view 3D reconstruction problem as jointly solving two coupled sub-tasks : i ) segmenting of image entities , e.g. planar regions , straight edge segments , and ii ) optimizing pixel-wise 3D scene model through the application of grammar rules over image entities . To reconstruct a new image , we design an efficient hybrid Monte Carlo ( HMC ) algorithm to simulate Markov Chain walking towards a posterior distribution .
The inverse of the forward model of image formation is used to propagate the gradient guidance from the low-resolution ( LR ) images to the HR image space . The gradient fields over multiple scales were exploited for more accurate edge localization in the reconstruction . The proposed SRR allows inter-volume motion during the MRI scans and can incorporate with the LR images with arbitrary orientations and displacements in the frequency space , such as orthogonal and origin-shifted scans .
HAADF-STEM data is increasingly being used in the physical sciences to study materials in 3D because it is free from the diffraction effects seen in Bright Field STEM data and satisfies the projection requirement for tomography . Typically , reconstruction is performed using Filtered Back Projection ( FBP ) or the SIRT algorithm . In this paper , we develop a Bayesian reconstruction algorithm for HAADF-STEM tomography which models the image formation , the noise characteristics of the measurement , and the inherent smoothness in the object .
The current release includes 3390 enzymes , 5645 compounds and 5207 reactions . The database is indispensable for the reconstruction of metabolic pathways in the completely sequenced organisms . The LIGAND database can be accessed through the WWW ( http://www.genome.ad.jp/dbget/ligand.html ) or may be downloaded by anonymous FTP ( ftp://kegg.genome.ad.jp/molecules/ligand/ ) .
In this paper , we propose a wavelet-based-interpolation algorithm , which improves the effect of Fourier image reconstruction for 2 D synthetic aperture radiometer . The core idea is to get the VF ( Visibility Function ) data on grid points by using the interpolation algorithm .
A method to greatly simplify the high-frame-rate ( HFR ) imaging system using a rotation of coordinates in image reconstruction was developed . A theory of Fourier image reconstruction was also developed and both in vitro ( on an ATS539 tissue-mimicking phantom ) and in vivo ( on a human heart ) experiments were performed to verify the theory .
In this context , we describe in detail an empirical and theoretical study carried out on the relation between the double-bounce effect of buildings and their orientation angle . Then , a novel approach to the automatic detection and reconstruction of building radar footprints from VHR SAR images is pre-sented . Unlike most of the methods presented in the literature , the developed method can extract and reconstruct building radar footprints from single VHR SAR images .
The development of methods for the automatic detection and reconstruction of building radar footprints fromsingle very high resolution ( VHR ) synthetic aperture radar ( SAR ) images is a difficult task for two main reasons : i ) the very high complexity of VHR SAR images ; and ii ) the need to develop efficient algorithms that can be applied to large images in order to be used in real applications . In this paper we present a novel method for automatic building detection , which also reconstructs the 2D radar footprint of the detected buildings .
Methods for joint activity reconstruction and attenuation reconstruction of time-of-flight ( TOF ) PET data provide an effective solution to attenuation correction when no ( or incomplete or inaccurate ) information on attenuation is available . One of the main barriers limiting use of these methods in clinical practice is their lack of validation in a relatively large patient database .
In order to allow real-time exploration of even the highest resolution data sets , adaptive techniques benefiting from the hierarchical nature of multi-resolution representations have gained special attention . In this paper we propose an adaptive approach to the fast reconstruction of iso-surfaces from regular volume data at arbitrary levels of detail . The algorithm has been designed to enable real-time navigation through complex structures while providing user-adjustable resolution levels .
2.5D map is a convenient and efficient approach to exploiting a massive three-dimensional ( 3D ) city model in web GIS . With the rapid development of oblique airborne photogrammetry and photo-based 3D reconstruction , 3D city models are becoming more and more accessible . 3D Geographic Information System ( GIS ) can support the interactive visualization of massive 3D city models on various platforms and devices .
PURPOSE CT image reconstruction requires accurate knowledge of the used geometry or image quality might be degraded by misalignment artifacts . To overcome this issue , an intrinsic method , that is , a method not requiring a dedicated calibration phantom , to perform a raw data-based misalignment correction for CT is proposed herein that does not require redundant data and hence is applicable to measurements with less than 180 ∘ plus fan-angle of data .
This tracks the dynamic joint configuration during walking . Direct experimental evaluation was provided by step length computation as well as complete motion reconstruction . This method has demonstrated stable long term tracking of walking and yields greater than 95 % accuracy for step length estimation .
A new scheme for 3D reconstruction of implicit surfaces from large scattered point sets based on the radial basis functions ( RBFs ) is proposed in this paper . The partition of unity ( POU ) method and a binary tree is used to organize the point sets into some overlapping local subdomains and reconstructing a local surface for each of the subdomains from non-disjunct subsets of the points , we use only a single point at the offset of the surface to avoid the trivial solution of RBF linear system .
The output of the decoder is added ( via a operation , defined in the sequel ) to a cumulative canvas matrix , which is ultimately used to reconstruct the image . The total number of time - steps consumed by the network before performing the reconstruction is a free parameter that must be specified in advance . For each image presented to the network , are initialised to learned biases , and the DRAW network iteratively computes the following equations for : where is the error image , is the concatenation of vectors and into a single vector , and denotes the logistic sigmoid function : .
Feature maps after each recursion are used to reconstruct the target high - resolution image ( HR ) . Reconstruction method ( layers dedicated to reconstruction ) is the same for all recursions . As each recursion leads to a different HR prediction , we combine all predictions resulting from different levels of recursions to deliver a more accurate final prediction .
The fact that these scores are much worse than the simple templated model indicates that further research is needed into better copying alone for content selection and better long term content ordering models . Test results are consistent with development results , indicating that the Conditional Copy model is most effective at BLEU , RG , and CS , and that reconstruction is quite helpful for improving the joint model . section : Human Evaluation
This work explores the challenges facing neural data - to - document generation by introducing a new dataset , and proposing various metrics for automatically evaluating content selection , generation , and ordering . We see that recent ideas in copying and reconstruction lead to improvements on this task , but that there is a significant gap even between these neural models and templated systems . We hope to motivate researchers to focus further on generation problems that are relevant both to content selection and surface realization , but may not be reflected clearly in the model 's perplexity .
In our first experiment , the task is to register pre - scanned object models to RGB - D scanning data for the Shelf & Tote benchmark in the Amazon Picking Challenge ( APC ) setting [ reference ] , as illustrated in Fig . 7 . This scenario is different from scene level reconstruction in the following two aspects : ( 1 ) object sizes and their geometric features are much smaller in scale and ( 2 ) the alignment here is from full pre - scanned models to partial scan data , instead of partial scans to partial scans . section : Method
Yang is a method of Yang et al . [ reference ] that employs sparse coding and sparse dictionaries for learning a compact representation of the LR - HR priors / training samples and for sharp HR reconstruction results . Zeyde is a method of Zeyde et al .
Other approaches , such as , train a top - down deconvolutional network to reconstruct the input image from a feature representation by minimizing the Euclidean reconstruction error in image space . However , there is inherent uncertainty in the reconstruction process , since the representations in higher layers of the DNN are trained to be invariant to irrelevant transformations and to ignore low - level details . With Euclidean training objective , the deconvolutional network tends to produce blurry images .
Since the videos in the Human3.6 M dataset are captured by stationary cameras , the subjects in the dataset do not rotate that much to provide alternative views for NRSFM algorithm to perform well . Another reason is that human pose reconstruction is a specialized problem in which constraints from human body structure apply . paragraph : Data pre - processing
Hypothesis 1 : The Denoising AE produces latent representations which are invariant to noise because it facilitates the reconstruction of de - noised images . We will demonstrate that this training strategy actually enforces invariance not only against noise but against a variety of different input augmentations .
One principled approach to unsupervised learning is generative probabilistic modeling . Not only do generative probabilistic models have the ability to create novel content , they also have a wide range of reconstruction related applications including inpainting theis2015generative , oord2016pixel , DBLP : conf / icml / Sohl - DicksteinW15 , denoising balle2015density , colorization zhang2016colorful , and super - resolution bruna2015super . As data of interest are generally high - dimensional and highly structured , the challenge in this domain is building models that are powerful enough to capture its complexity yet still trainable .
We present a multisensory method for estimating the transformation of a mobile phone between two images taken from its camera . Pose estimation is a necessary step for applications such as 3D reconstruction and panorama construction , but detecting and matching robust features can be computationally expensive . In this paper we propose a method for combining the inertial sensors ( accelerometers and gyroscopes ) of a mobile phone with its camera to provide a fast and accurate pose estimation .
3D Human Face Reconstruction or 3D Human Face Modeling is a construction of 3D geometry graphics of human face model which has been a very challenging research in both computer vision and computer graphics for over the last three decades . Researchers have proposed many methods for 3D human face reconstruction by using or not using 3D scanner .
Registration of human cloud points is a key step in 3D human reconstruction . So , this paper proposes using depth camera to acquire human point cloud .
We propose a novel end-to-end deep learning framework , capable of 3D human shape reconstruction from a 2D image without the need of a 3D prior parametric model . We employ a “ prior-less ” representation of the human shape using unordered point clouds .
A method for obtaining point data by means of analysis of the position relations of view items , and an algorithm for pretreating input projection view data are presented in this paper . Good result are obtained when the algorithm is used in 3D solid reconstruction .
It is essential to generate efficient three-dimensional ( 3D ) spatial data model in 3D modeling , because the data processed is multiple in scale and large in volume . Based on investigation some existed strategies of 3D solid reconstruction and the features of section plane views , the technology of 3D entity model reconstruction from section plane views based on ES is given in this paper . From the view of engineering application , this paper introduces the reconstruction model of ES and the expression way for the frame knowledge and mainly discusses the discretization of the 3D model face and reasoning algorithm based on discretization meshwork model .
The evaluation shows that when the method is used with traditional feature detector and descriptor , it increases the number of detected keypoints , provides higher repeatability and improves the localization accuracy . We also demonstrate that such features will lead to more accurate and complete reconstructions when used in the application of 3D visual reconstruction .
The MA3HO workshop is aimed at taking a leap forward in emerging research of multimedia access of 3D human objects , merging researchers in 3D graphics , 3D object recognition and retrieval , Multimedia , with attention to application fields where humans are highly significant , such as security , surveillance and biometry , animation and entertainment , video retrieval , sport analytics , natural interaction , cultural heritage , augmented and virtual reality and world wide web . Main subjects addressed are among the others : 3D human objects reconstruction from 2D views 3D pose estimation from 2D information 2D to 3D human object matching 3D human object categorization 3D people identification and re-identification 3D object/face similarity matching , indexing , and mining Feature extraction for 3D model segmentation Feature extraction for 3D motion detection and behavior classification 3D shape descriptors Retrieval with large distributed and heterogeneous 3D datasets and benchmarking Semantics-driven 3D object retrieval and classification 3D natural interfaces and search modalities The workshop has attracted 18 good quality submissions fairly distributed among different countries : China , Japan , Canada , USA , France , Italy and Germany . Many of the key arguments of the workshop call were addressed .
Further on , we show that a simplified meshing can be applied to the patch group borders which enables a GPU centric real-time implementation . The presented algorithm is tested on real world test shoot data for the case of 3D reconstruction of humans .
Then , we describe how we exploit the 3D information to select harder matching pairs and hard non - matching pairs with larger variability . subsection : BoW and 3D reconstruction The retrieval engine used in the work of Schonberger et al . builds upon BoW with fast spatial verification .
Due to the fact that the information contained in one single image is not sufficient for 3D shape reconstruction , the existing model can not reconstruct 3D models very well . To tackle this problem , we propose a novel model which effectively utilizes the category information of objects to improve the performance of network on single view 3D reconstruction . Our model consists of two parts : rough shape generation network ( RSGN ) and category comparison network ( CCN ) .
The latent space formed by the Seq2Seq encoder encodes both part structure and fine part geometry . The decoder can be adapted to perform several generative tasks including shape autoencoding , interpolation , novel shape generation , and single-view 3D reconstruction , where the generated shapes are all composed of meaningful parts .
Existing works on single-image 3D reconstruction mainly focus on shape recovery . In this work , we study a new problem , that is , simultaneously recovering 3D shape and surface color from a single image , namely " colorful 3D reconstruction " .
From a single view , humans are able to hallucinate the full 3D shape of the object in the image , even if it is from a novel , unseen category . Contemporary AI systems for single-image 3D reconstruction often lack this ability , because the shape priors they learned is often tied to the training object classes . In this paper , we study the task of single-image 3D reconstruction , but attempting to recover the full 3D shape of an object outside the training categories .
Color encoded structured light is an important means for single-shot 3D reconstruction . In this article , we present a pseudorandom array-based structured light method .
Single-view 3D shape reconstruction is an important but challenging problem , mainly for two reasons . First , as shape annotation is very expensive to acquire , current methods rely on synthetic data , in which ground-truth 3D annotation is easy to obtain .
We present a new algorithm for single camera 3D reconstruction , or 3D input for human-computer interfaces , based on precise tracking of an elongated object , such as a pen , having a pattern of colored bands . To configure the system , the user provides no more than one labelled image of a handmade pointer , measurements of its colored bands , and the camera 's pinhole projection matrix .
This paper introduces single-image 3D scene reconstruction from water reflection photography , i.e. , images capturing direct and water-reflected real-world scenes . Water reflection offers an additional viewpoint to the direct sight , collectively forming a stereo pair .
The theories and techniques of 3D Reconstruction are one of the most important problems in the computer vision , Single- image-based 3D Reconstruction because of lacking enough several informations but hard attain to expect the result . This paper aims at the symmetry characteristic of most objects , putting forward make use of to imagine the symmetry flat surface according to Projection Inverse Transformation principle , handing over from the artificial to specify with each other to have already known that three picture that rightnesses are a little bit symmetries sit the mark and spaces to sit to mark the Projection Inverse Transformation matrix , Inverse the 3D position that the object surface characteristic order , then make use of the software of UG to carry out 3D models .
At the heart of our approach is a small number of grammar rules that can describe the most common geometric structures , e.g. , two straights lines being co-linear or orthogonal , or that a line lying on a planar region etc . With these grammar rules , we re-frame single-view 3D reconstruction problem as jointly solving two coupled sub-tasks : i ) segmenting of image entities , e.g. planar regions , straight edge segments , and ii ) optimizing pixel-wise 3D scene model through the application of grammar rules over image entities . To reconstruct a new image , we design an efficient hybrid Monte Carlo ( HMC ) algorithm to simulate Markov Chain walking towards a posterior distribution .
Isotropic MRI Super-Resolution Reconstruction with Multi-scale Gradient Field Prior Bayesian tomographic reconstruction for high angle annular dark field (HAADF) scanning transmission electron microscopy (STEM) LIGAND: chemical database of enzyme reactions Wavelet Interpolation Algorithm for Synthetic Aperture Radiometer 2C-5 Simplification of High Frame Rate Imaging System with Coordinate Rotation Advanced Methods for the Analysis of Radar Sounder and VHR SAR Signals Building detection and radar footprint reconstruction from single VHR SAR images Joint Reconstruction of Activity and Attenuation in Time-of-Flight PET: A Quantitative Analysis Real-time exploration of regular volume data by adaptive reconstruction of isosurfaces Generating Orthorectified Multi-Perspective 2.5D Maps to Facilitate Web GIS-Based Visualization and Exploitation of Massive 3D City Models Technical Note: Intrinsic raw data‐based CT misalignment correction without redundant data Gait analysis using 3D motion reconstruction with an activity-specific tracking protocol Implicit fitting and smoothing using radial basis functions with partition of unity DRAW: A Recurrent Neural Network For Image Generation Deeply-Recursive Convolutional Network for Image Super-Resolution Challenges in Data-to-Document Generation 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions Seven Ways to Improve Example-Based Single Image Super Resolution Stacked Generative Adversarial Networks Exploiting Temporal Information for 3D Human Pose Estimation Implicit 3D Orientation Learning for 6D Object Detection from RGB Images Density estimation using Real NVP Multisensory embedded pose estimation 3D human face reconstruction using depth sensor of Kinect 2 Acquisition and Registration of Human Point Cloud Based on Depth Camera Prior-less 3D Human Shape Reconstruction with an Earth Mover’s Distance Informed CNN An Algorithm for Evaluating Hidden Points' Data of the Projection View During 3D Solid Reconstruction 3D entity model reconstruction from section plane views based on ES Fast Motion Deblurring for Feature Detection and Matching Using Inertial Measurements Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding, J-HGBU@MM 2011, Scottsdale, Arizona, USA, December 1, 2011 Visibility-driven patch group generation CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples Single View 3D Reconstruction with Category Information Learning PQ-NET: A Generative Part Seq2Seq Network for 3D Shapes Im2Avatar: Colorful 3D Reconstruction from a Single Image Learning to Reconstruct Shapes from Unseen Categories A single-shot structured light means by encoding both color and geometrical features Domain-Adaptive Single-View 3D Reconstruction Do-It-Yourself Single Camera 3D Pointer Input Device Shape from Water Reflection Inverse of 3D from Image Point Based On Projection Inverse Transformation A Stochastic Image Grammar for Fine-Grained 3D Scene Reconstruction
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MICCAI (3) SSP IGARSS ICASSP CAD/Graphics ICML 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) EMNLP 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ECCV ECCV ICLR WACV MIG IC3D ECCV IFTC CRV IJCAI
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