The tool reads multiple DICOM image stacks acquired in any angulation into a consistent patient coordinate frame and allows the user to select regions to be locally motion corrected . It combines algorithms for slice motion estimation , bias field inconsistency correction and 3D volume reconstruction from multiple scattered slice stacks . The tool is built onto the RView ( http://rview.colin-studholme.net ) medical image display software and allows the user to inspect slice stacks , and apply both stack and slice level motion estimation that incorporates temporal constraints based on slice timing and interleave information read from the DICOM data . We perform a number of numerical experiments in order to demonstrate the efficiency of this method . HighlightsA fast and efficient numerical method for 3D volume reconstruction is proposed . The proposed numerical scheme can use large time step sizes . This has significant potential to improve the understanding of the growth patterns and the spatial arrangement of diseased cells , and enhance the study of biomechanical behavior of the tissue structures towards better treatments ( e.g. tissue-engineering applications ) . Methods : This paper evaluates three strategies for 3D reconstruction from sets of two dimensional ( 2D ) histological sections with different stains , by combining methods of 2D multi-stain registration and 3D volumetric reconstruction from same stain sections . Setting and Design : Highly accurate 3D volumetric reconstruction is still an open research topic where the main difficulty is usually related to merging some rough estimations with high frequency details . One of the most promising methods is the fusion between multi-view stereo and photometric stereo images . From the requirements of rapid reconstruction in virtual endoscopy system , the solution on 3D volume data reconstruction based on constant distance function that satisfied real-time request has been proposed . The algorithm constructs field function using constant distance on the basis of effective contour extraction , and then utilizes scanning line fill algorithm to reconstruct volume data rapidly by one scanning . The proposed system could provide personalized ultrasound scanning and extend the scan area to include the axillary lymph nodes , which has not been achieved by existing automatic ultrasound scanning systems . The probe positions and orientations allows 3D volume-data reconstruction . In the future , computer aided detection software can be integrated with the proposed system for fully automated ultrasound breast screening . An axially distributed sensing system is a 3D sensing and imaging where the sensors are distributed along the optical axis . In this system , a prior knowledge of exact sensor positions was required for 3D volume image reconstruction . In this paper , we overview unknown sensor position estimation method and present an axially distributed sensing with unknown sensor positions . Currently , 3D cone-beam CT image reconstruction speed is still a severe limitation for clinical application . The computational power of modern graphics processing units ( GPUs ) has been harnessed to provide impressive acceleration of 3D volume image reconstruction . For extra large data volume exceeding the physical graphic memory of GPU , a straightforward compromise is to divide data volume into blocks . To provide fast display and manipulation of the 3D reconstruction of volumetric data , our system uses surface rendering which reduces 3D data sets to geometric primitives that have been implemented in the SGIs hardware , so a better performance of rendering speed is obtained . Our system supports both basic and advanced navigation features of medical data sets , including translating , rotating , zooming in/out , advanced cutting functions ( transparent alignment , multi-plane cut , cut in any orientation and depth ) , classification of tissues , and centralization function . The work tackles first the problem of 2D surfacic cortical reconstruction , proposing an anisotropic edge-preserving vector diffusion algorithm , inspired by the relatively recent emergence of diffusion algorithms in the image processing community . Subsequently , the 3D volumetric reconstruction problem is attacked under the perspective of statistical estimation . A thorough analysis of the reconstruction bounds , based on the introduction of the sensitivity functions and on the Cramer-Rao lower bound , results in principles for optimal system design . In order to detect unsafe objects in a static scene , our method further infers hidden and situated causes ( disturbances ) in the scene , and then introduces intuitive physical mechanics to predict possible effects ( e.g. , falls ) as consequences of the disturbances . In experiments , we demonstrate that the algorithm achieves a substantially better performance for ( i ) object segmentation , ( ii ) 3D volumetric recovery , and ( iii ) scene understanding with respect to other state-of-the-art methods . We also compare the safety prediction from the intuitive mechanics model with human judgement . The PSP cephalograms were imported into Dolphin ( Chatsworth , CA , USA ) and the 3D volumetric data set was imported into Dolphin 3D ( Version 2.3 , Chatsworth , CA , USA ) . Using Dolphin 3D , planar cephalograms as well as 3D volumetric surface reconstructions were ( 3D CBCT ) generated . The linear measurements between landmarks of each three modalities were then computed by a single observer three times . A new method for obtaining a three-dimensional volumetric reconstruction from a set of views improving the classical Shape from Silhouette method ( SFS ) is presented . SFS approaches can be easily accelerated through hardware and software techniques but they are very sensible to errors arising during calibration and segmentation processes so they present difficulties when dealing with real images . The entire network is end - to - end trainable with only weak supervision . We demonstrate the superior representation power of our nonlinear 3DMM over its linear counterpart , and its contribution to face alignment and 3D reconstruction . ⌊⌋ ⌈⌉ The majority of extant works resort to regular representations such as volumetric grids or collection of images ; however , these representations obscure the natural invariance of 3D shapes under geometric transformations , and also suffer from a number of other issues . In this paper we address the problem of 3D reconstruction from a single image , generating a straight - forward form of output - point cloud coordinates . Along with this problem arises a unique and interesting issue , that the groundtruth shape for an input image may be ambiguous . In this paper we address the problem of generating the 3D geometry of an object based on a single image of that object . We explore generative networks for 3D geometry based on a point cloud representation . A point cloud representation may not be as efficient in representing the underlying continuous 3D geometry as compared to a CAD model using geometric primitives or even a simple mesh , but for our purposes it has many advantages , A point cloud is a simple , uniform structure that is easier to learn , as it does not have to encode multiple primitives or combinatorial connectivity patterns . Their huge advantage , as unsupervised methods , is they do not need 3D training data , instead they can learn a linear basis for the 3D poses purely from 2D data . Their main drawback is their need for significant camera movement throughout the sequence to guarantee accurate 3D reconstruction . Recent work on NRSfM applied to human pose estimation has focused on escaping these limitations by the use of a linear model to represent shape variations of the human body . document : Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression 3D face reconstruction is a fundamental Computer Vision problem of extraordinary difficulty . Current systems often assume the availability of multiple facial images ( sometimes from the same subject ) as input , and must address a number of methodological challenges such as establishing dense correspondences across large facial poses , expressions , and non - uniform illumination . 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 . used transfer learning to transfer the knowledge learned for 2D human pose estimation to the task of 3D pose estimation . Pavlakos et al . extended the stacked - hourglass network originally designed to predict 2D heatmaps of each joint to make it predict 3D volumetric heatmaps . Tome et al . also extended a 2D pose estimator called Convolutional Pose Machine ( CPM ) to make it predict 3D pose . In multi-view 3D object retrieval , each object is characterized by a group of 2D images captured from different views . Rather than using hand-crafted features , in this paper , we take advantage of the strong discriminative power of convolutional neural network to learn an effective 3D object representation tailored for this retrieval task . This paper describes a novel approach to multi-view 3D object retrieval . To represent each view of the object , its edge contours are extracted at different levels of scale . In view-based 3D shape recognition , extracting discriminative visual representation of 3D shapes from projected images is considered the core problem . Projections with low discriminative ability can adversely influence the final 3D shape representation . Fast and accurate detection of 3D shapes is a fundamental task of robotic systems for intelligent tracking and automatic control . View-based 3D shape recognition has attracted increasing attention because human perceptions of 3D objects mainly rely on multiple 2D observations from different viewpoints . However , most existing multi-view-based cognitive computation methods use straightforward pairwise comparisons among the projected images then follow with weak aggregation mechanism , which results in heavy computation cost and low recognition accuracy . Extracting and aggregating discriminative image features is a key challenge for 3D multi-view object recognition and retrieval tasks . In this paper , we propose aggregated deep CNNs ( ADCNN ) model to address the limitations associated with projecting 3D models into multiple 2D images and their resulting high-dimensional representations . Consequently , our database is useful for recognizing the 3D pose and 3D shape of objects from 2D images . We also provide baseline experiments on four tasks : region proposal generation , 2D object detection , joint 2D detection and 3D object pose estimation , and image-based 3D shape retrieval , which can serve as baselines for future research using our database . Our database is available online at http://cvgl.stanford.edu/projects/objectnet3d . We introduce a new method for automatic selection of optimal views in a shape-based method of multi-view 3D object representation and recognition . A 3D object is recognised by an optimum number of images taken from different views . Research in this area has focused on the case where elements of the set are represented by feature vectors , and far less emphasis has been given to the common case where set elements themselves adhere to certain symmetries . That case is relevant to numerous applications , from deblurring image bursts to multi-view 3D shape recognition and reconstruction . In this paper , we present a principled approach to learning sets of general symmetric elements . A common and standard 3D tool database is constructed . The hybrid networks of Convolutional Neural Networks ( CNNs ) and Extreme Learning Machine ( ELM ) are developed for multiple view based 3D shape recognition . This framework utilizes the composited advantages of deep CNN architecture with the robust ELM auto-encoder feature representation , as well as the fast ELM classifier . Projective analysis is an important solution in three-dimensional ( 3D ) shape retrieval , since human visual perceptions of 3D shapes rely on various 2D observations from different viewpoints . Although multiple informative and discriminative views are utilized , most projection-based retrieval systems suffer from heavy computational cost , and thus can not satisfy the basic requirement of scalability for search engines . In a view-based three-dimensional ( 3-D ) model retrieval task , extracting discriminative high-level features of models from projected images is considered as an effective approach . The challenge of view-based 3-D shape retrieval is that the shape information of each view is limited due to information deficiency in projection . Traditional methods in this direction mostly convert the model into a panoramic view , making it hard to recognize the original shape . To examine our model ’s ability to generate high - resolution 3D shapes with realistic details , we design a task that involves shape generation and shape interpolation . We add Gaussian noise to the learned latent codes on test data taken from ModelNet40 and then use our model to generate “ unseen ” samples that are similar to the input voxel . The other solution is developing objective quality metrics , which attempt to model the Human Visual System ( HVS ) in order to assess perceptual quality . Although several 2D quality metrics have been proposed for still images and videos , in the case of 3D efforts are only at the initial stages . In this paper , we propose a new full-reference quality metric for 3D content . As large collections of 3D garments continue to grow , analyzing and exploring shape variations is significant but challenging . In this paper , we propose a semi-supervised learning method for semantic segmentation and labeling of 3D garments . The key idea in this work is to address the data challenge for 3D garment analysis using semi-supervised learning method which can label parts in various 3D garments . 3D representations include the third dimension of an image which is the depth information of an object . This paper presents an overall approach to 3D modelling of jewellery from the uncalibrated images . The datasets are taken from different viewing planes at different intervals . The experimental results confirm that our method outperforms , in general , the existing ones . Although we have concentrated in this paper on coarse 3D head pose problem , the proposed approach could also be applied to other classification tasks for objects characterized by large variance in their appearance . FD-MIMO is more advantageous when amalgamated with massive MIMO systems , in that it exploits the additional degrees of freedom offered by a large number of antennas in the elevation . To facilitate the evaluation of these systems , a large effort in 3D channel modeling is needed . This paper aims at providing a summary of the recent 3GPP activity around 3D channel modeling . Thanks to the availability of 3D-capable televisions and bluray players , 3D content is made accessible in the home . Recently , an extension of the H.264/AVC video coding standard has been defined for encoding 3D video content . This extension , called Multiview Video Coding , allows inter-view prediction resulting in a better compression efficiency . Pointing at the problem about boundary contact constraint of FEA numerical simulation of 3D massive forming , matrix transform algorithm has been proposed and numerical experiment and FEA simulation for upsetting formation of spur gear have been completed . AbstractCreating High Dynamic Range ( HDR ) images of static scenes is a common procedure nowadays that combinesseveral Low Dynamic Range ( LDR ) images . However , HDR video and 3D content creation and management isan active , unsolved research topic . This work proposes a method to build HDR images from Low Dynamic Range(LDR)inputimagestakenwithmulti-viewcameras . A particular type of fast , transitory ' thumbnail ' sketch would appear to be important . If this is so then attention to its modern counterpart in the latest 3D , multi-user , immersive virtual design studios is overdue if they are to support the cognitive processes of creativity vital to design . A typical scenario for multi-view 3D consists of several 3D video sequences captured simultaneously from the same scene with the help of multiple cameras from different positions and through different angles . Due to the extremely high bandwidth of multi-view/3D video , rate control is one of the key factors influencing its quality . 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 . 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 . Imitating the practice in image detection , it is straightforward to enumerate candidate 3D boxes by sliding windows or by 3D region proposal networks such as . However , the computational complexity of 3D search typically grows cubically with respect to resolution and becomes too expensive for large scenes or real - time applications such as autonomous driving . Instead , in this work , we reduce the search space following the dimension reduction principle : we take the advantage of mature 2D object detectors ( Fig . ’s amodal oriented 3D bounding box by using a box regression PointNet together with a preprocessing transformer network . paragraph : Learning - based 3D Alignment by T - Net Even though we have aligned segmented object points according to their centroid position , we find that the origin of the mask coordinate frame ( Fig . Deep pose estimation . This work describes a deep network trained to estimate the 6DoF of 3D faces viewed in single images . Deep learning is increasingly used for similar purposes , though typically focusing on general object classes . Song learn to classify 3D bounding box proposals generated by a 3D sliding window using synthetically - generated 3D features . A follow - up study uses a 3D variant of the Region Proposal Network to generate 3D proposals and uses a 3D ConvNet to process the voxelized point cloud . A similar approach by Li focuses on detecting vehicles and processes the voxelized input with a 3D fully convolutional network . 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 . The developments presented here concern the complete re-design of the scene graph editor unit , and the C++ implementation of a new spatial renderer based on the JUCE framework . EVERTims now functions as a Blender add-on to support real-time auralization of any 3D room model , both for its creation in Blender and its exploration in the Blender Game Engine . The EVERTims framework is published as open source software . We also contribute a supervised machine learning method for object classification based on sets of planar shapes . Chapter 4 introduces a method for 3D geometric modeling of indoor scenes . We first partition the space using primitive shapes detected from permanent structures . The task of inferring the 3D layout of indoor scenes from images has seen many recent advancements . Understanding the basic 3D geometry of these environments is important for higher level applications , such as object recognition and robot navigation . High-quality 3D reconstruction of large-scale indoor scene is the key to combine Simultaneous Localization And Mapping ( SLAM ) with other applications , such as building inspection and construction monitoring . However , the requirement of global consistency brings challenges to both localization and mapping . Furthermore , Microsoft offers a full set of drivers and interfaces for the using of Kinect in Windows 7 and Windows 8 . These entire features make Kinect a promising 3D data source that can be used in 3D reconstruction of room sized scenes and navigation of robots . NeevEngine was used for development of Mat Isles , a mechanical laboratory simulation where the player goes through the steps involved in Tensile Testing Experiment . The game engine provides support for 3D room rendering , animations , presentation of educational content and presentation of quizzes . All the quiz data is stored in a MySql database with the help of PHP webservices which can be later retrieved . The Personal Environment Controller ( PECo ) is a digital assistant system that provides the user with a unified facility for organizing as well as accessing the different media repositories at his disposal and direct manipulation of the physical environment ( projectors , TV sets , hi-fi systems , Lights , shutters , etc . ) . To interact with the environment , PECo´s user interface is based on the 3D visualization of the room , allowing the user the intuitive selection of a device , basing his selection on the device position and orientation within the 3D model of the room . The main purpose of this thesis is the development and implementation of a system that can dynamically update this user interface , providing PECo with a model that describes the environment . Panoramic 3D reconstruction of an indoor environment is an important issue for navigation and the safety control for a robot . In this study , we examine an acquisition device constructed of a regular CCD camera and a 2D laser range scanner , along with a technique for panoramic 3D reconstruction using a data fusion algorithm based on an energy minimization framework . In order to validate the effect of our post-rectification approach , we apply it to RGB-D simultaneous localization and mapping ( SLAM ) in an indoor environment . Experimental results show that once our post-rectification approach is employed , the RGB-D SLAM system can perform a more accurate and better visual effect 3D reconstruction of indoor scenes than other state-of-the-art methods .
The tool reads multiple DICOM image stacks acquired in any angulation into a consistent patient coordinate frame and allows the user to select regions to be locally motion corrected . It combines algorithms for slice motion estimation , bias field inconsistency correction and 3D volume reconstruction from multiple scattered slice stacks . The tool is built onto the RView ( http://rview.colin-studholme.net ) medical image display software and allows the user to inspect slice stacks , and apply both stack and slice level motion estimation that incorporates temporal constraints based on slice timing and interleave information read from the DICOM data .
We perform a number of numerical experiments in order to demonstrate the efficiency of this method . HighlightsA fast and efficient numerical method for 3D volume reconstruction is proposed . The proposed numerical scheme can use large time step sizes .
This has significant potential to improve the understanding of the growth patterns and the spatial arrangement of diseased cells , and enhance the study of biomechanical behavior of the tissue structures towards better treatments ( e.g. tissue-engineering applications ) . Methods : This paper evaluates three strategies for 3D reconstruction from sets of two dimensional ( 2D ) histological sections with different stains , by combining methods of 2D multi-stain registration and 3D volumetric reconstruction from same stain sections . Setting and Design :
Highly accurate 3D volumetric reconstruction is still an open research topic where the main difficulty is usually related to merging some rough estimations with high frequency details . One of the most promising methods is the fusion between multi-view stereo and photometric stereo images .
From the requirements of rapid reconstruction in virtual endoscopy system , the solution on 3D volume data reconstruction based on constant distance function that satisfied real-time request has been proposed . The algorithm constructs field function using constant distance on the basis of effective contour extraction , and then utilizes scanning line fill algorithm to reconstruct volume data rapidly by one scanning .
The proposed system could provide personalized ultrasound scanning and extend the scan area to include the axillary lymph nodes , which has not been achieved by existing automatic ultrasound scanning systems . The probe positions and orientations allows 3D volume-data reconstruction . In the future , computer aided detection software can be integrated with the proposed system for fully automated ultrasound breast screening .
An axially distributed sensing system is a 3D sensing and imaging where the sensors are distributed along the optical axis . In this system , a prior knowledge of exact sensor positions was required for 3D volume image reconstruction . In this paper , we overview unknown sensor position estimation method and present an axially distributed sensing with unknown sensor positions .
Currently , 3D cone-beam CT image reconstruction speed is still a severe limitation for clinical application . The computational power of modern graphics processing units ( GPUs ) has been harnessed to provide impressive acceleration of 3D volume image reconstruction . For extra large data volume exceeding the physical graphic memory of GPU , a straightforward compromise is to divide data volume into blocks .
To provide fast display and manipulation of the 3D reconstruction of volumetric data , our system uses surface rendering which reduces 3D data sets to geometric primitives that have been implemented in the SGIs hardware , so a better performance of rendering speed is obtained . Our system supports both basic and advanced navigation features of medical data sets , including translating , rotating , zooming in/out , advanced cutting functions ( transparent alignment , multi-plane cut , cut in any orientation and depth ) , classification of tissues , and centralization function .
The work tackles first the problem of 2D surfacic cortical reconstruction , proposing an anisotropic edge-preserving vector diffusion algorithm , inspired by the relatively recent emergence of diffusion algorithms in the image processing community . Subsequently , the 3D volumetric reconstruction problem is attacked under the perspective of statistical estimation . A thorough analysis of the reconstruction bounds , based on the introduction of the sensitivity functions and on the Cramer-Rao lower bound , results in principles for optimal system design .
In order to detect unsafe objects in a static scene , our method further infers hidden and situated causes ( disturbances ) in the scene , and then introduces intuitive physical mechanics to predict possible effects ( e.g. , falls ) as consequences of the disturbances . In experiments , we demonstrate that the algorithm achieves a substantially better performance for ( i ) object segmentation , ( ii ) 3D volumetric recovery , and ( iii ) scene understanding with respect to other state-of-the-art methods . We also compare the safety prediction from the intuitive mechanics model with human judgement .
The PSP cephalograms were imported into Dolphin ( Chatsworth , CA , USA ) and the 3D volumetric data set was imported into Dolphin 3D ( Version 2.3 , Chatsworth , CA , USA ) . Using Dolphin 3D , planar cephalograms as well as 3D volumetric surface reconstructions were ( 3D CBCT ) generated . The linear measurements between landmarks of each three modalities were then computed by a single observer three times .
A new method for obtaining a three-dimensional volumetric reconstruction from a set of views improving the classical Shape from Silhouette method ( SFS ) is presented . SFS approaches can be easily accelerated through hardware and software techniques but they are very sensible to errors arising during calibration and segmentation processes so they present difficulties when dealing with real images .
The entire network is end - to - end trainable with only weak supervision . We demonstrate the superior representation power of our nonlinear 3DMM over its linear counterpart , and its contribution to face alignment and 3D reconstruction . ⌊⌋ ⌈⌉
The majority of extant works resort to regular representations such as volumetric grids or collection of images ; however , these representations obscure the natural invariance of 3D shapes under geometric transformations , and also suffer from a number of other issues . In this paper we address the problem of 3D reconstruction from a single image , generating a straight - forward form of output - point cloud coordinates . Along with this problem arises a unique and interesting issue , that the groundtruth shape for an input image may be ambiguous .
In this paper we address the problem of generating the 3D geometry of an object based on a single image of that object . We explore generative networks for 3D geometry based on a point cloud representation . A point cloud representation may not be as efficient in representing the underlying continuous 3D geometry as compared to a CAD model using geometric primitives or even a simple mesh , but for our purposes it has many advantages , A point cloud is a simple , uniform structure that is easier to learn , as it does not have to encode multiple primitives or combinatorial connectivity patterns .
Their huge advantage , as unsupervised methods , is they do not need 3D training data , instead they can learn a linear basis for the 3D poses purely from 2D data . Their main drawback is their need for significant camera movement throughout the sequence to guarantee accurate 3D reconstruction . Recent work on NRSfM applied to human pose estimation has focused on escaping these limitations by the use of a linear model to represent shape variations of the human body .
document : Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression 3D face reconstruction is a fundamental Computer Vision problem of extraordinary difficulty . Current systems often assume the availability of multiple facial images ( sometimes from the same subject ) as input , and must address a number of methodological challenges such as establishing dense correspondences across large facial poses , expressions , and non - uniform illumination .
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 .
used transfer learning to transfer the knowledge learned for 2D human pose estimation to the task of 3D pose estimation . Pavlakos et al . extended the stacked - hourglass network originally designed to predict 2D heatmaps of each joint to make it predict 3D volumetric heatmaps . Tome et al . also extended a 2D pose estimator called Convolutional Pose Machine ( CPM ) to make it predict 3D pose .
In multi-view 3D object retrieval , each object is characterized by a group of 2D images captured from different views . Rather than using hand-crafted features , in this paper , we take advantage of the strong discriminative power of convolutional neural network to learn an effective 3D object representation tailored for this retrieval task .
This paper describes a novel approach to multi-view 3D object retrieval . To represent each view of the object , its edge contours are extracted at different levels of scale .
In view-based 3D shape recognition , extracting discriminative visual representation of 3D shapes from projected images is considered the core problem . Projections with low discriminative ability can adversely influence the final 3D shape representation .
Fast and accurate detection of 3D shapes is a fundamental task of robotic systems for intelligent tracking and automatic control . View-based 3D shape recognition has attracted increasing attention because human perceptions of 3D objects mainly rely on multiple 2D observations from different viewpoints . However , most existing multi-view-based cognitive computation methods use straightforward pairwise comparisons among the projected images then follow with weak aggregation mechanism , which results in heavy computation cost and low recognition accuracy .
Extracting and aggregating discriminative image features is a key challenge for 3D multi-view object recognition and retrieval tasks . In this paper , we propose aggregated deep CNNs ( ADCNN ) model to address the limitations associated with projecting 3D models into multiple 2D images and their resulting high-dimensional representations .
Consequently , our database is useful for recognizing the 3D pose and 3D shape of objects from 2D images . We also provide baseline experiments on four tasks : region proposal generation , 2D object detection , joint 2D detection and 3D object pose estimation , and image-based 3D shape retrieval , which can serve as baselines for future research using our database . Our database is available online at http://cvgl.stanford.edu/projects/objectnet3d .
We introduce a new method for automatic selection of optimal views in a shape-based method of multi-view 3D object representation and recognition . A 3D object is recognised by an optimum number of images taken from different views .
Research in this area has focused on the case where elements of the set are represented by feature vectors , and far less emphasis has been given to the common case where set elements themselves adhere to certain symmetries . That case is relevant to numerous applications , from deblurring image bursts to multi-view 3D shape recognition and reconstruction . In this paper , we present a principled approach to learning sets of general symmetric elements .
A common and standard 3D tool database is constructed . The hybrid networks of Convolutional Neural Networks ( CNNs ) and Extreme Learning Machine ( ELM ) are developed for multiple view based 3D shape recognition . This framework utilizes the composited advantages of deep CNN architecture with the robust ELM auto-encoder feature representation , as well as the fast ELM classifier .
Projective analysis is an important solution in three-dimensional ( 3D ) shape retrieval , since human visual perceptions of 3D shapes rely on various 2D observations from different viewpoints . Although multiple informative and discriminative views are utilized , most projection-based retrieval systems suffer from heavy computational cost , and thus can not satisfy the basic requirement of scalability for search engines .
In a view-based three-dimensional ( 3-D ) model retrieval task , extracting discriminative high-level features of models from projected images is considered as an effective approach . The challenge of view-based 3-D shape retrieval is that the shape information of each view is limited due to information deficiency in projection . Traditional methods in this direction mostly convert the model into a panoramic view , making it hard to recognize the original shape .
To examine our model ’s ability to generate high - resolution 3D shapes with realistic details , we design a task that involves shape generation and shape interpolation . We add Gaussian noise to the learned latent codes on test data taken from ModelNet40 and then use our model to generate “ unseen ” samples that are similar to the input voxel .
The other solution is developing objective quality metrics , which attempt to model the Human Visual System ( HVS ) in order to assess perceptual quality . Although several 2D quality metrics have been proposed for still images and videos , in the case of 3D efforts are only at the initial stages . In this paper , we propose a new full-reference quality metric for 3D content .
As large collections of 3D garments continue to grow , analyzing and exploring shape variations is significant but challenging . In this paper , we propose a semi-supervised learning method for semantic segmentation and labeling of 3D garments . The key idea in this work is to address the data challenge for 3D garment analysis using semi-supervised learning method which can label parts in various 3D garments .
3D representations include the third dimension of an image which is the depth information of an object . This paper presents an overall approach to 3D modelling of jewellery from the uncalibrated images . The datasets are taken from different viewing planes at different intervals .
The experimental results confirm that our method outperforms , in general , the existing ones . Although we have concentrated in this paper on coarse 3D head pose problem , the proposed approach could also be applied to other classification tasks for objects characterized by large variance in their appearance .
FD-MIMO is more advantageous when amalgamated with massive MIMO systems , in that it exploits the additional degrees of freedom offered by a large number of antennas in the elevation . To facilitate the evaluation of these systems , a large effort in 3D channel modeling is needed . This paper aims at providing a summary of the recent 3GPP activity around 3D channel modeling .
Thanks to the availability of 3D-capable televisions and bluray players , 3D content is made accessible in the home . Recently , an extension of the H.264/AVC video coding standard has been defined for encoding 3D video content . This extension , called Multiview Video Coding , allows inter-view prediction resulting in a better compression efficiency .
Pointing at the problem about boundary contact constraint of FEA numerical simulation of 3D massive forming , matrix transform algorithm has been proposed and numerical experiment and FEA simulation for upsetting formation of spur gear have been completed .
AbstractCreating High Dynamic Range ( HDR ) images of static scenes is a common procedure nowadays that combinesseveral Low Dynamic Range ( LDR ) images . However , HDR video and 3D content creation and management isan active , unsolved research topic . This work proposes a method to build HDR images from Low Dynamic Range(LDR)inputimagestakenwithmulti-viewcameras .
A particular type of fast , transitory ' thumbnail ' sketch would appear to be important . If this is so then attention to its modern counterpart in the latest 3D , multi-user , immersive virtual design studios is overdue if they are to support the cognitive processes of creativity vital to design .
A typical scenario for multi-view 3D consists of several 3D video sequences captured simultaneously from the same scene with the help of multiple cameras from different positions and through different angles . Due to the extremely high bandwidth of multi-view/3D video , rate control is one of the key factors influencing its quality .
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 .
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 .
Imitating the practice in image detection , it is straightforward to enumerate candidate 3D boxes by sliding windows or by 3D region proposal networks such as . However , the computational complexity of 3D search typically grows cubically with respect to resolution and becomes too expensive for large scenes or real - time applications such as autonomous driving . Instead , in this work , we reduce the search space following the dimension reduction principle : we take the advantage of mature 2D object detectors ( Fig .
’s amodal oriented 3D bounding box by using a box regression PointNet together with a preprocessing transformer network . paragraph : Learning - based 3D Alignment by T - Net Even though we have aligned segmented object points according to their centroid position , we find that the origin of the mask coordinate frame ( Fig .
Deep pose estimation . This work describes a deep network trained to estimate the 6DoF of 3D faces viewed in single images . Deep learning is increasingly used for similar purposes , though typically focusing on general object classes .
Song learn to classify 3D bounding box proposals generated by a 3D sliding window using synthetically - generated 3D features . A follow - up study uses a 3D variant of the Region Proposal Network to generate 3D proposals and uses a 3D ConvNet to process the voxelized point cloud . A similar approach by Li focuses on detecting vehicles and processes the voxelized input with a 3D fully convolutional network .
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 .
The developments presented here concern the complete re-design of the scene graph editor unit , and the C++ implementation of a new spatial renderer based on the JUCE framework . EVERTims now functions as a Blender add-on to support real-time auralization of any 3D room model , both for its creation in Blender and its exploration in the Blender Game Engine . The EVERTims framework is published as open source software .
We also contribute a supervised machine learning method for object classification based on sets of planar shapes . Chapter 4 introduces a method for 3D geometric modeling of indoor scenes . We first partition the space using primitive shapes detected from permanent structures .
The task of inferring the 3D layout of indoor scenes from images has seen many recent advancements . Understanding the basic 3D geometry of these environments is important for higher level applications , such as object recognition and robot navigation .
High-quality 3D reconstruction of large-scale indoor scene is the key to combine Simultaneous Localization And Mapping ( SLAM ) with other applications , such as building inspection and construction monitoring . However , the requirement of global consistency brings challenges to both localization and mapping .
Furthermore , Microsoft offers a full set of drivers and interfaces for the using of Kinect in Windows 7 and Windows 8 . These entire features make Kinect a promising 3D data source that can be used in 3D reconstruction of room sized scenes and navigation of robots .
NeevEngine was used for development of Mat Isles , a mechanical laboratory simulation where the player goes through the steps involved in Tensile Testing Experiment . The game engine provides support for 3D room rendering , animations , presentation of educational content and presentation of quizzes . All the quiz data is stored in a MySql database with the help of PHP webservices which can be later retrieved .
The Personal Environment Controller ( PECo ) is a digital assistant system that provides the user with a unified facility for organizing as well as accessing the different media repositories at his disposal and direct manipulation of the physical environment ( projectors , TV sets , hi-fi systems , Lights , shutters , etc . ) . To interact with the environment , PECo´s user interface is based on the 3D visualization of the room , allowing the user the intuitive selection of a device , basing his selection on the device position and orientation within the 3D model of the room . The main purpose of this thesis is the development and implementation of a system that can dynamically update this user interface , providing PECo with a model that describes the environment .
Panoramic 3D reconstruction of an indoor environment is an important issue for navigation and the safety control for a robot . In this study , we examine an acquisition device constructed of a regular CCD camera and a 2D laser range scanner , along with a technique for panoramic 3D reconstruction using a data fusion algorithm based on an energy minimization framework .
In order to validate the effect of our post-rectification approach , we apply it to RGB-D simultaneous localization and mapping ( SLAM ) in an indoor environment . Experimental results show that once our post-rectification approach is employed , the RGB-D SLAM system can perform a more accurate and better visual effect 3D reconstruction of indoor scenes than other state-of-the-art methods .
SLIMMER: SLIce MRI motion estimation and reconstruction tool for studies of fetal anatomy Fast and efficient narrow volume reconstruction from scattered data 3D reconstruction of multiple stained histology images A Differential Volumetric Approach to Multi-View Photometric Stereo Algorithm of 3D volume data reconstruction based on constant distance Automated Conformal Ultrasound Scanning for Breast Screening Unknown sensor position estimation in axially distributed sensing and 3D imaging GPU-Based 3D Cone-Beam CT Image Reconstruction for Large Data Volume Feature-based interactive visualization of volumetric medical data Robust focalized brain activity reconstruction using electroencephalograms Scene Understanding by Reasoning Stability and Safety Comparative linear accuracy and reliability of cone beam CT derived 2-dimensional and 3-dimensional images constructed using an orthodontic volumetric rendering program Reliable 3D reconstruction extending pixel-level certainty measures Nonlinear 3D Face Morphable Model A Point Set Generation Network for 3D Object Reconstruction from a Single Image Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image 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 Exploiting Temporal Information for 3D Human Pose Estimation Multi-View 3D Object Retrieval With Deep Embedding Network Multi-view free-form 3-D object retrieval with incomplete data Learning Discriminative 3D Shape Representations by View Discerning Networks Multi-View CNN Feature Aggregation with ELM Auto-Encoder for 3D Shape Recognition Aggregated Deep Convolutional Neural Networks for Multi-View 3D Object Retrieval ObjectNet3D: A Large Scale Database for 3D Object Recognition Automatic view selection in multi-view object recognition On Learning Sets of Symmetric Elements Intelligent Machine Tools Recognition Based on Hybrid CNNs and ELMs Networks GIFT: Towards Scalable 3D Shape Retrieval Improved Panoramic Representation via Bidirectional Recurrent View Aggregation for Three-Dimensional Model Retrieval Learning a Hierarchical Latent-Variable Model of 3D Shapes An efficient human visual system based quality metric for 3D video Semantic Segmentation and Labeling of 3D Garments 3D modelling of a jewellery and its virtual try-on A Parameter-Free Locality Sensitive Discriminant Analysis and Its Application to Coarse 3D Head Pose Estimation Spatial Correlation Characterization of a Full Dimension Massive MIMO System Comparing objective visual quality impairment detection in 2D and 3D video sequences Boundary friction and slipping constraint treatment of numerical simulation for massive forming Patch-based registration for auto-stereoscopic HDR content creation Is Sketching Still Relevant in Virtual Design Studios A new rate distortion model for multi-view/3D video coding 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 Frustum PointNets for 3D Object Detection from RGB-D Data FacePoseNet: Making a Case for Landmark-Free Face Alignment PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation 3D Face Morphable Models "In-the-Wild" EVERTims: Open source framework for real-time auralization in VR Geometric modeling of indoor scenes from acquired point data. (Modélisation géométrique de scènes intérieures à partir de nuage de points) Top-Down Bayesian Inference of Indoor Scenes High quality 3D reconstruction of indoor environments using RGB-D sensors A Study in 3D-Reconstruction Using Kinect Sensor NeevEngine : using XNA game studio for serious game design and development Conceptualization and Realization of a Framework for Model-Based, Automatic User Interface Generation Panoramic 3D Reconstruction by Fusing Color Intensity and Laser Range Data A Post-Rectification Approach of Depth Images of Kinect v2 for 3D Reconstruction of Indoor Scenes
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Medical Imaging: Image Processing SMC FUZZ-IEEE 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE International Conference on Computer Vision (ICCV) ArXiv ECCV MMSP ICUMT ECCV (8) ICPR ELM 2018 International Conference on 3D Vision (3DV) ISVC (2) GLOBECOM QoMEX ICME Workshops 2017 IEEE International Conference on Computer Vision (ICCV) ArXiv 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Audio Mostly Conference Advanced Topics in Computer Vision
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