, Multi-Baseline 2d View Synthesis And 3d Depth Estimation Using Commodity Graphics Hardware . We present a new method for using commodity graphics hardware to achieve real-time , on-line , 2D view synthesis or 3D depth estimation from two or more calibrated cameras . Our method combines a 3D plane-sweeping approach with 2D multi-resolution color consistency tests . In this research work , we propose to use the disparity map that is computed from two or multiple images , in order to monitor the depth information in an object or event under surveillance using WMSN . Our system is based on distributed wireless sensors allowing us to notably reduce the computational time needed for 3D depth reconstruction , thus permitting the success of real time solutions . Each pair of sensors will capture images for a targeted place/object and will operate a Stereo Matching in order to create a Disparity Map . Recent advances in 3D depth cameras using either structured light sensor or time-of-flight sensor have made it possible to capture depth images in real time . Thanks to these innovations , nowadays even a consumer-level device equipped with a pair of spatially aligned RGB camera and depth camera can already simultaneously provide high-quality visual ( RGB ) and depth information of a scene . Recent advances in 3D depth cameras using either structured light sensor or time-of-flight sensor have made it possible to capture depth images in real time . Thanks to these innovations , nowadays even a consumer-level device equipped with a pair of spatially aligned RGB camera and depth camera can already simultaneously provide high-quality visual ( RGB ) and depth information of a scene . Some of the latest smartphones also include depth-ranging sensors that can be used for 3D model generation . Light-based detection and ranging ( lidar ) technologies are yet another field where 3D depth acquisition is important . Realtime 3D scenery detection and ranging has become an important issue for the emerging field of autonomous navigation and driving applications . The result of the process ( depth information ) can be used to guide various tasks such as synthetic aperture radar ( SAR ) , magnetic resonance imaging ( MRI ) , automatic inspection , reverse engineering , 3D robot navigation , interferometry and so on . The obtained information can be used to guide various processes such as robotic manipulation , automatic inspection , inverse engineering , 3D depth map for navigation and virtual reality applications ( Gokstorp , 1995 ) . Depending on the application , a simple 3D description is necessary to understand the scene and perform the desired task , while in other cases a dense map or detailed information of the object ’s shape is necessary . Experiment results show that the high resolution depth map of visual camera is successfully generated and of high quality . Keywords-Time-of-Flight ; stereo calibration ; 3D depth map estimation Experimental results are presented to show the feasibility of this method . To the best of our knowledge , this is the first time that statistics of the ray intensity-angle is used for 3D depth mapping . 3D depth measurement is important in robot navigation , remote manipulation and auto-driving system etc . It can be used to avoid obstacles and identify objects . We propose a new iterative surface evolution algorithm for multiview stereo . Starting from an embedding space such as the visual hull , we will first conduct robust 3D depth estimation ( represented as 3D points ) based on image correlation . A fast implicit distance function-based region growing method is then employed to extract an initial shape estimation based on these 3D points . To result crisp flow boundaries , LiteFlowNet regularizes flow field through feature - driven local convolution ( f - lcon ) . With its lightweight , accurate , and fast flow computation , we expect that LiteFlowNet can be deployed to many applications such as motion segmentation , action recognition , SLAM , 3D reconstruction and more . Acknowledgement . [ reference ] ( left ) , as VRN without alignment . We believe that this further confirms that spatial alignment is of paramount importance when performing 3D reconstruction in this way . section : Ablation studies section : Related Work 3D reconstruction has been well studied based on the multi - view geometry ( MVG ) in the literature . The major research directions include structure from motion ( SfM ) for large - scale high - quality reconstruction and simultaneous localization and mapping ( SLAM ) for navigation . Related work . Convolutional networks have emerged as an effective approach to localizing body joints of people in images and have also been extended for joint estimation of body configurations over time , and 3D pose estimation in outdoor environments in multi - camera setting . Current approaches are increasingly effective for estimating body configurations of single people achieving high accuracies on this task , but are still failing on fast moving and articulated limbs . In addition , they drain the batteries of mobile devices , being an active sensor . It is therefore desirable to rely only on color images for 3D pose estimation , even if it is more challenging . Recent methods [ reference ][ reference ][ reference ] work by identifying the ' object coordinates ' of the pixels , which are the pixels ' 3D coordinates in a coordinate system related to the object [ reference ] . Such systems make simplifying assumptions and make decisions in the absence of context from other sensors . Inspired by the successes of deep learning for handling diverse raw sensory input , we propose an early fusion model for 3D box estimation , which directly learns to combine image and depth information optimally . Various combinations of cameras and 3D sensors are widely used in the field , and it is desirable to have a single algorithm that generalizes to as many different problem settings as possible . the 3D IoU with ground truth box will then be dominated by the angle error . Ideally all three terms ( center , size , heading ) should be jointly optimized for best 3D box estimation ( under IoU metric ) . To resolve this problem we propose a novel regularization loss , the corner loss : In essence , the corner loss is the sum of the distances between the eight corners of a predicted box and a ground truth box . Many of the above methods require multiple images and / or additional human - provided information . Some approaches have attempted to minimize human involvement by developing weakly - supervised schemes , making use of image silhouettes to conduct 3D object reconstruction . Of the few unsupervised neural - based approaches that exist , the T - L network is one of the most important , combining a convolutional autoencoder with an image regressor to encode a unified vector representation of a given 2D image . document : Lifting from the Deep : Convolutional 3D Pose Estimation from a Single Image We propose a unified formulation for the problem of 3D human pose estimation from a single raw RGB image that reasons jointly about 2D joint estimation and 3D pose reconstruction to improve both tasks . We take an integrated approach that fuses probabilistic knowledge of 3D human pose with a multi - stage CNN architecture and uses the knowledge of plausible 3D landmark locations to refine the search for better 2D locations . The new architecture is then initialized by using the weights with those found in CPM ’s model for all preexisting layers , with the new layers randomly initialized . After retraining , CPMs return per - pixel estimates of landmark locations , while the techniques for 3D estimation ( described in the next section ) make use of 2D locations . To transform these belief maps into locations , we select the most confident pixel as the location of each landmark This is primarily attributed to the fact that the community has made a considerable effort to collect and annotate facial images captured under unconstrained conditions ( commonly referred to as “ in - the - wild ” ) and to the discriminative methodologies that can capitalise on the availability of such large amount of data . Nevertheless , discriminative techniques can not be applied for 3D facial shape estimation “ in - the - wild ” , due to lack of ground - truth data . 3D facial shape estimation from single images has attracted the attention of many researchers the past twenty years . Recent evaluations on IJB - A are also based on convolutional networks and mirror the top performing approaches on LFW and YouTubeFaces . Recent approaches include deep networks using triplet loss similarity and joint Bayesian metric learning , and five pose specialized sub - networks with 3D pose rendering . Face - BCNN applies the bilinear CNN architecture to face identification , publishing the earliest results on IJB - A. Transfer learning has been well studied in the literature , and we refer to a comprehensive survey on the topic . Opaque object detection and image acquisition are perfectly done with the techniques collecting reflections from the object . However , transparent object detection is a challenge due to the complex nature of light interacting with it . The characteristics of light such as absorption , transmission , reflection , and refraction in transparent objects makes its acquisition challenging . However , transparent objects sensing still remains a challenging problem in the area of computational imaging and optical engineering . As an indispensable part of 3-D modeling , transparent object sensing is a long-standing research topic , which aims to reconstruct the surface shape of a given transparent object using various kinds of measurement methods . In this paper , we put forward a new method for the sensing of such objects . We introduce the light field camera for transparent object classification . We model the distortion of the light field caused by a transparent object . In order to improve the accuracy and efficiency of robot grasping , we propose a new method for transparent object detection and location that utilize depth image , RGB image and IR image . In detection process , an active depth sensor ( RealSense ) is firstly employed to retrieve the transparent candidates from the depth image and the corresponding candidates in the RGB image and IR image are then extracted separately . With the phase difference image retrieved using the DIC technique , the phase map of the examined object can be approximated by integrating the phase difference . Experimental results show the feasibility of using the transmitted DIC for transparent object measurement . The results show that the height of a transparent structure measured using the DIC method is quite close to those measured using AFM while those measured using the white light interference method results in much larger measurement than all others . The method includes the steps that sight information of at least one user in a space is acquired ; at least one transparent object existing in the space is determined at least according to the sight information . By means of the technical scheme , the transparent objects in the space are determined according to the sight information of the users , and a basis is provided for transparent-object-based application like transparent object modeling . Ambiguity can be due to changes in lighting , viewpoint , and backgrounds , each of which brings challenges to existing object pose estimation algorithms . In this work , we propose LIT , a two-stage method for transparent object pose estimation using light-field sensing and photorealistic rendering . LIT employs multiple filters specific to light-field imagery in deep networks to capture transparent material properties , with robust depth and pose estimators based on generative sampling . They either possess limited sample size such as merely a thousand of images without manual annotations , or they generate all images by using computer graphics method ( i.e. not real image ) . To address this important problem , this work proposes a large-scale dataset for transparent object segmentation , named Trans10 K , consisting of 10,428 images of real scenarios with carefully manual annotations , which are 10 times larger than the existing datasets . The transparent objects in Trans10 K are extremely challenging due to high diversity in scale , viewpoint and occlusion as shown in Fig. 1 . Next , a background difference algorithm is used to extract the pest object from the images . Finally , based on the detection and the matching of feature points in the binocular object images , the 3D reconstruction of the pest data is achieved . Experimental results showed that the algorithm is efficient and accurate , the average error of 3D reconstruction result is less than 0.5 mm , and the percentage error is less than 1.5 % . We describe an algorithm for 3D interactive image segmentation by non-rigid implicit template deformation , with two main original features . First , our formulation incorporates user input as inside/outside labeled points to drive the deformation and improve both robustness and accuracy . This provides flexibility , allows structures to be designed to only support microgravity and allows sensing systems to be configured ( and reconfigured ) based on needs identified during the mission . This paper presents work undertaken to identify materials that are well-suited to in-space 3D printing . Materials commonly used for aerospace hardware were examined to assess their suitability . This paper proposes a Deep Learning ( DL ) approach towards the automated CAD of cranial implants , allowing the design process to be less user-dependent and even less time-consuming . The problem of reconstructing a cranial defect , which is essentially filling in a region in a skull , was posed as a 3D shape completion task and , to solve it , a Volumetric Convolutional Denoising Autoencoder was implemented using the open-source DL framework PyTorch . The autoencoder was trained on 3D skull models obtained by processing an open-access dataset of Magnetic Resonance Imaging brain scans . Automated 3D prototyping capability is at critical demands for rapid production across multiple industries . While traditional 3D object reconstruction approaches have been heavily relying on depth maps , which are either costly to acquire or inaccurate to approximate . Less complex techniques , such as Doppler-based imaging ( which can not provide lateral flow measurements ) and basic speckle tracking algorithms ( which have poor lateral accuracy ) , are incapable of producing high quality 3D measurements . In this paper , we first extend a technique designed to improve lateral flow accuracy for 2D velocity vector estimation , the synthetic lateral phase method , to 3D ( SLP-3D ) . We then show that a straightforward implementation of this algorithm is too computationally complex for modern systems . vii , 121 leaves%%%%In this thesis , the Constrained Resource Planning ( CRP ) model is employed to solve Three-Dimensional ( 3D ) packing problems . An efficient packing program 3dPack has been implemented which could be used to assist human packing in the parcel industries . It places the students in a spatial dimension , provides enhanced interactivity and allows for synchronous communication . In order to achieve the new Second Life 3D virtual teaching and learning environment , a practical , English teaching , project-based virtual environment is implemented . Features , composition , and the realization method of this 3D virtual learning environment for language learning are introduced . Texturing 3D models using casual images has gained importance in the last decade , with the advent of huge databases of images . We present a novel approach for performing this task , which manages to account for the 3D geometry of the photographed object . Abstract The paper describes a numerical method for the solution of stationary gas dynamics 3D spatial equations on unstructured grids that is designed for multiple graphics processing unit ( GPU ) computational architecture . Discretization of governing equations is done using first and second order TVD schemes . document : 3D Face Morphable Models “ In - the - Wild ” 3D Morphable Models ( 3DMMs ) are powerful statistical models of 3D facial shape and texture , and among the state - of - the - art methods for reconstructing facial shape from single images . ” images is currently very difficult by using modern acquisition devices . That is , even though there is a considerable improvement in 3D acquisition devices , they still can not operate in arbitrary conditions . Hence , all the current 3D facial databases have been captured in controlled conditions . Given the input shape , the goal of is to regress the shape primitive parameters which is achieved via a CNN . The method of extends classical work on heatmap regression by proposing a 4D representation for regressing the location of sparse 3D landmarks for human pose estimation . Different from , we demonstrate that a 3D volumetric representation is particular effective for learning dense 3D facial geometry . With respect to 3D object generation from 2D images , commonly used methods can be roughly grouped into two categories : 3D voxel prediction and mesh - based methods . The 3D - R2N2 model represents a more recent approach to the task , which involves training a recurrent neural network to predict 3D voxels from one or more 2D images . also takes a recurrent network - based approach , but receives a depth image as input rather than normal 2D images . In the original PointNet implementation , all fully connected layers are followed by a batch normalization layer . However , we found that batch normalization hampers the 3D bounding box estimation performance . Batch normalization aims to eliminate the scale and bias in its input data , but for the task of 3D regression , the absolute numerical values of the point locations are helpful . paragraph : 3D Shape Segmentation An important application of our framework is to obtain semantic part segmentation of 3D shapes in a supervised fashion . Along this track , most previous methods employ traditional machine learning techniques and construct classifiers based on geometric features . Similar to Mask - RCNN , which achieves instance segmentation by binary classification of pixels in image regions , we realize 3D instance segmentation using a PointNet - based network on point clouds in frustums . Based on 3D instance segmentation , we are able to achieve residual based 3D localization . That is , rather than regressing the absolute 3D location of the object whose offset from the sensor may vary in large ranges ( e.g. from 5 m to beyond 50 m in KITTI data ) , we predict the 3D bounding box center in a local coordinate system
, Multi-Baseline 2d View Synthesis And 3d Depth Estimation Using Commodity Graphics Hardware . We present a new method for using commodity graphics hardware to achieve real-time , on-line , 2D view synthesis or 3D depth estimation from two or more calibrated cameras . Our method combines a 3D plane-sweeping approach with 2D multi-resolution color consistency tests .
In this research work , we propose to use the disparity map that is computed from two or multiple images , in order to monitor the depth information in an object or event under surveillance using WMSN . Our system is based on distributed wireless sensors allowing us to notably reduce the computational time needed for 3D depth reconstruction , thus permitting the success of real time solutions . Each pair of sensors will capture images for a targeted place/object and will operate a Stereo Matching in order to create a Disparity Map .
Recent advances in 3D depth cameras using either structured light sensor or time-of-flight sensor have made it possible to capture depth images in real time . Thanks to these innovations , nowadays even a consumer-level device equipped with a pair of spatially aligned RGB camera and depth camera can already simultaneously provide high-quality visual ( RGB ) and depth information of a scene .
Recent advances in 3D depth cameras using either structured light sensor or time-of-flight sensor have made it possible to capture depth images in real time . Thanks to these innovations , nowadays even a consumer-level device equipped with a pair of spatially aligned RGB camera and depth camera can already simultaneously provide high-quality visual ( RGB ) and depth information of a scene .
Some of the latest smartphones also include depth-ranging sensors that can be used for 3D model generation . Light-based detection and ranging ( lidar ) technologies are yet another field where 3D depth acquisition is important . Realtime 3D scenery detection and ranging has become an important issue for the emerging field of autonomous navigation and driving applications .
The result of the process ( depth information ) can be used to guide various tasks such as synthetic aperture radar ( SAR ) , magnetic resonance imaging ( MRI ) , automatic inspection , reverse engineering , 3D robot navigation , interferometry and so on . The obtained information can be used to guide various processes such as robotic manipulation , automatic inspection , inverse engineering , 3D depth map for navigation and virtual reality applications ( Gokstorp , 1995 ) . Depending on the application , a simple 3D description is necessary to understand the scene and perform the desired task , while in other cases a dense map or detailed information of the object ’s shape is necessary .
Experiment results show that the high resolution depth map of visual camera is successfully generated and of high quality . Keywords-Time-of-Flight ; stereo calibration ; 3D depth map estimation
Experimental results are presented to show the feasibility of this method . To the best of our knowledge , this is the first time that statistics of the ray intensity-angle is used for 3D depth mapping .
3D depth measurement is important in robot navigation , remote manipulation and auto-driving system etc . It can be used to avoid obstacles and identify objects .
We propose a new iterative surface evolution algorithm for multiview stereo . Starting from an embedding space such as the visual hull , we will first conduct robust 3D depth estimation ( represented as 3D points ) based on image correlation . A fast implicit distance function-based region growing method is then employed to extract an initial shape estimation based on these 3D points .
To result crisp flow boundaries , LiteFlowNet regularizes flow field through feature - driven local convolution ( f - lcon ) . With its lightweight , accurate , and fast flow computation , we expect that LiteFlowNet can be deployed to many applications such as motion segmentation , action recognition , SLAM , 3D reconstruction and more . Acknowledgement .
[ reference ] ( left ) , as VRN without alignment . We believe that this further confirms that spatial alignment is of paramount importance when performing 3D reconstruction in this way . section : Ablation studies
section : Related Work 3D reconstruction has been well studied based on the multi - view geometry ( MVG ) in the literature . The major research directions include structure from motion ( SfM ) for large - scale high - quality reconstruction and simultaneous localization and mapping ( SLAM ) for navigation .
Related work . Convolutional networks have emerged as an effective approach to localizing body joints of people in images and have also been extended for joint estimation of body configurations over time , and 3D pose estimation in outdoor environments in multi - camera setting . Current approaches are increasingly effective for estimating body configurations of single people achieving high accuracies on this task , but are still failing on fast moving and articulated limbs .
In addition , they drain the batteries of mobile devices , being an active sensor . It is therefore desirable to rely only on color images for 3D pose estimation , even if it is more challenging . Recent methods [ reference ][ reference ][ reference ] work by identifying the ' object coordinates ' of the pixels , which are the pixels ' 3D coordinates in a coordinate system related to the object [ reference ] .
Such systems make simplifying assumptions and make decisions in the absence of context from other sensors . Inspired by the successes of deep learning for handling diverse raw sensory input , we propose an early fusion model for 3D box estimation , which directly learns to combine image and depth information optimally . Various combinations of cameras and 3D sensors are widely used in the field , and it is desirable to have a single algorithm that generalizes to as many different problem settings as possible .
the 3D IoU with ground truth box will then be dominated by the angle error . Ideally all three terms ( center , size , heading ) should be jointly optimized for best 3D box estimation ( under IoU metric ) . To resolve this problem we propose a novel regularization loss , the corner loss : In essence , the corner loss is the sum of the distances between the eight corners of a predicted box and a ground truth box .
Many of the above methods require multiple images and / or additional human - provided information . Some approaches have attempted to minimize human involvement by developing weakly - supervised schemes , making use of image silhouettes to conduct 3D object reconstruction . Of the few unsupervised neural - based approaches that exist , the T - L network is one of the most important , combining a convolutional autoencoder with an image regressor to encode a unified vector representation of a given 2D image .
document : Lifting from the Deep : Convolutional 3D Pose Estimation from a Single Image We propose a unified formulation for the problem of 3D human pose estimation from a single raw RGB image that reasons jointly about 2D joint estimation and 3D pose reconstruction to improve both tasks . We take an integrated approach that fuses probabilistic knowledge of 3D human pose with a multi - stage CNN architecture and uses the knowledge of plausible 3D landmark locations to refine the search for better 2D locations .
The new architecture is then initialized by using the weights with those found in CPM ’s model for all preexisting layers , with the new layers randomly initialized . After retraining , CPMs return per - pixel estimates of landmark locations , while the techniques for 3D estimation ( described in the next section ) make use of 2D locations . To transform these belief maps into locations , we select the most confident pixel as the location of each landmark
This is primarily attributed to the fact that the community has made a considerable effort to collect and annotate facial images captured under unconstrained conditions ( commonly referred to as “ in - the - wild ” ) and to the discriminative methodologies that can capitalise on the availability of such large amount of data . Nevertheless , discriminative techniques can not be applied for 3D facial shape estimation “ in - the - wild ” , due to lack of ground - truth data . 3D facial shape estimation from single images has attracted the attention of many researchers the past twenty years .
Recent evaluations on IJB - A are also based on convolutional networks and mirror the top performing approaches on LFW and YouTubeFaces . Recent approaches include deep networks using triplet loss similarity and joint Bayesian metric learning , and five pose specialized sub - networks with 3D pose rendering . Face - BCNN applies the bilinear CNN architecture to face identification , publishing the earliest results on IJB - A. Transfer learning has been well studied in the literature , and we refer to a comprehensive survey on the topic .
Opaque object detection and image acquisition are perfectly done with the techniques collecting reflections from the object . However , transparent object detection is a challenge due to the complex nature of light interacting with it . The characteristics of light such as absorption , transmission , reflection , and refraction in transparent objects makes its acquisition challenging .
However , transparent objects sensing still remains a challenging problem in the area of computational imaging and optical engineering . As an indispensable part of 3-D modeling , transparent object sensing is a long-standing research topic , which aims to reconstruct the surface shape of a given transparent object using various kinds of measurement methods . In this paper , we put forward a new method for the sensing of such objects .
We introduce the light field camera for transparent object classification . We model the distortion of the light field caused by a transparent object .
In order to improve the accuracy and efficiency of robot grasping , we propose a new method for transparent object detection and location that utilize depth image , RGB image and IR image . In detection process , an active depth sensor ( RealSense ) is firstly employed to retrieve the transparent candidates from the depth image and the corresponding candidates in the RGB image and IR image are then extracted separately .
With the phase difference image retrieved using the DIC technique , the phase map of the examined object can be approximated by integrating the phase difference . Experimental results show the feasibility of using the transmitted DIC for transparent object measurement . The results show that the height of a transparent structure measured using the DIC method is quite close to those measured using AFM while those measured using the white light interference method results in much larger measurement than all others .
The method includes the steps that sight information of at least one user in a space is acquired ; at least one transparent object existing in the space is determined at least according to the sight information . By means of the technical scheme , the transparent objects in the space are determined according to the sight information of the users , and a basis is provided for transparent-object-based application like transparent object modeling .
Ambiguity can be due to changes in lighting , viewpoint , and backgrounds , each of which brings challenges to existing object pose estimation algorithms . In this work , we propose LIT , a two-stage method for transparent object pose estimation using light-field sensing and photorealistic rendering . LIT employs multiple filters specific to light-field imagery in deep networks to capture transparent material properties , with robust depth and pose estimators based on generative sampling .
They either possess limited sample size such as merely a thousand of images without manual annotations , or they generate all images by using computer graphics method ( i.e. not real image ) . To address this important problem , this work proposes a large-scale dataset for transparent object segmentation , named Trans10 K , consisting of 10,428 images of real scenarios with carefully manual annotations , which are 10 times larger than the existing datasets . The transparent objects in Trans10 K are extremely challenging due to high diversity in scale , viewpoint and occlusion as shown in Fig. 1 .
Next , a background difference algorithm is used to extract the pest object from the images . Finally , based on the detection and the matching of feature points in the binocular object images , the 3D reconstruction of the pest data is achieved . Experimental results showed that the algorithm is efficient and accurate , the average error of 3D reconstruction result is less than 0.5 mm , and the percentage error is less than 1.5 % .
We describe an algorithm for 3D interactive image segmentation by non-rigid implicit template deformation , with two main original features . First , our formulation incorporates user input as inside/outside labeled points to drive the deformation and improve both robustness and accuracy .
This provides flexibility , allows structures to be designed to only support microgravity and allows sensing systems to be configured ( and reconfigured ) based on needs identified during the mission . This paper presents work undertaken to identify materials that are well-suited to in-space 3D printing . Materials commonly used for aerospace hardware were examined to assess their suitability .
This paper proposes a Deep Learning ( DL ) approach towards the automated CAD of cranial implants , allowing the design process to be less user-dependent and even less time-consuming . The problem of reconstructing a cranial defect , which is essentially filling in a region in a skull , was posed as a 3D shape completion task and , to solve it , a Volumetric Convolutional Denoising Autoencoder was implemented using the open-source DL framework PyTorch . The autoencoder was trained on 3D skull models obtained by processing an open-access dataset of Magnetic Resonance Imaging brain scans .
Automated 3D prototyping capability is at critical demands for rapid production across multiple industries . While traditional 3D object reconstruction approaches have been heavily relying on depth maps , which are either costly to acquire or inaccurate to approximate .
Less complex techniques , such as Doppler-based imaging ( which can not provide lateral flow measurements ) and basic speckle tracking algorithms ( which have poor lateral accuracy ) , are incapable of producing high quality 3D measurements . In this paper , we first extend a technique designed to improve lateral flow accuracy for 2D velocity vector estimation , the synthetic lateral phase method , to 3D ( SLP-3D ) . We then show that a straightforward implementation of this algorithm is too computationally complex for modern systems .
vii , 121 leaves%%%%In this thesis , the Constrained Resource Planning ( CRP ) model is employed to solve Three-Dimensional ( 3D ) packing problems . An efficient packing program 3dPack has been implemented which could be used to assist human packing in the parcel industries .
It places the students in a spatial dimension , provides enhanced interactivity and allows for synchronous communication . In order to achieve the new Second Life 3D virtual teaching and learning environment , a practical , English teaching , project-based virtual environment is implemented . Features , composition , and the realization method of this 3D virtual learning environment for language learning are introduced .
Texturing 3D models using casual images has gained importance in the last decade , with the advent of huge databases of images . We present a novel approach for performing this task , which manages to account for the 3D geometry of the photographed object .
Abstract The paper describes a numerical method for the solution of stationary gas dynamics 3D spatial equations on unstructured grids that is designed for multiple graphics processing unit ( GPU ) computational architecture . Discretization of governing equations is done using first and second order TVD schemes .
document : 3D Face Morphable Models “ In - the - Wild ” 3D Morphable Models ( 3DMMs ) are powerful statistical models of 3D facial shape and texture , and among the state - of - the - art methods for reconstructing facial shape from single images .
” images is currently very difficult by using modern acquisition devices . That is , even though there is a considerable improvement in 3D acquisition devices , they still can not operate in arbitrary conditions . Hence , all the current 3D facial databases have been captured in controlled conditions .
Given the input shape , the goal of is to regress the shape primitive parameters which is achieved via a CNN . The method of extends classical work on heatmap regression by proposing a 4D representation for regressing the location of sparse 3D landmarks for human pose estimation . Different from , we demonstrate that a 3D volumetric representation is particular effective for learning dense 3D facial geometry .
With respect to 3D object generation from 2D images , commonly used methods can be roughly grouped into two categories : 3D voxel prediction and mesh - based methods . The 3D - R2N2 model represents a more recent approach to the task , which involves training a recurrent neural network to predict 3D voxels from one or more 2D images . also takes a recurrent network - based approach , but receives a depth image as input rather than normal 2D images .
In the original PointNet implementation , all fully connected layers are followed by a batch normalization layer . However , we found that batch normalization hampers the 3D bounding box estimation performance . Batch normalization aims to eliminate the scale and bias in its input data , but for the task of 3D regression , the absolute numerical values of the point locations are helpful .
paragraph : 3D Shape Segmentation An important application of our framework is to obtain semantic part segmentation of 3D shapes in a supervised fashion . Along this track , most previous methods employ traditional machine learning techniques and construct classifiers based on geometric features .
Similar to Mask - RCNN , which achieves instance segmentation by binary classification of pixels in image regions , we realize 3D instance segmentation using a PointNet - based network on point clouds in frustums . Based on 3D instance segmentation , we are able to achieve residual based 3D localization . That is , rather than regressing the absolute 3D location of the object whose offset from the sensor may vary in large ranges ( e.g. from 5 m to beyond 50 m in KITTI data ) , we predict the 3D bounding box center in a local coordinate system
A Unified Approach To Real-Time, Multi-Resolution, Multi-Baseline 2d View Synthesis And 3d Depth Estimation Using Commodity Graphics Hardware Efficient and accurate monitoring of the depth information in a Wireless Multimedia Sensor Network based surveillance Feature Learning from RGB-D Data for Multimedia Applications [Editorial] Guest Editorial: Feature Learning from RGB-D Data for Multimedia Applications Video-Based Point-Cloud-Compression Standard in MPEG: From Evidence Collection to Committee Draft [Standards in a Nutshell] Three Dimensional Reconstruction Strategies Using a Profilometrical Approach based on Fourier Transform Improved 3D Depth Image Estimation Algorithm for Visual Camera Profilometry and optical slicing by passive three-dimensional imaging. Depth measurement based on omni-directional camera and pixel offset An Iterative Surface Evolution Algorithm for Multiview Stereo LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images ArtTrack: Articulated Multi-Person Tracking in the Wild BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation Frustum PointNets for 3D Object Detection from RGB-D Data Learning a Hierarchical Latent-Variable Model of 3D Shapes Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image 3D Face Morphable Models "In-the-Wild" Template adaptation for face verification and identification Transparent Object Detection Using Single-pixel Imaging and Compressive Sensing Transparent object sensing with enhanced prior from deep convolutional neural network Light field distortion feature for transparent object classification Transparent object detection and location based on RGB-D camera Feasibility and challenge for measurement of transparent object by adopting differential interference contrast technology Information processing method, information processing device and user equipment LIT: Light-Field Inference of Transparency for Refractive Object Localization Segmenting Transparent Objects in the Wild Algorithm for 3D reconstruction of agriculture field pests based on binocular stereo vision Real-Time 3D Image Segmentation by User-Constrained Template Deformation Consideration of materials for creating 3D printed space sensors and systems Automated Computer-aided Design of Cranial Implants Using a Deep Volumetric Convolutional Denoising Autoencoder Structure-Aware 3D Shape Synthesis from Single-View Images A low complexity scheme for accurate 3D velocity estimation in ultrasound systems Three dimensional container packing using constrained resource planning Implementation of a project-based 3D virtual learning environment for English language learning FlexiStickers: photogrammetric texture mapping using casual images Implicit method for the solution of supersonic and hypersonic 3D flow problems with Lower-Upper Symmetric-Gauss-Seidel preconditioner on multiple graphics processing units 3D Face Morphable Models "In-the-Wild" Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression Learning a Hierarchical Latent-Variable Model of 3D Shapes PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation Frustum PointNets for 3D Object Detection from RGB-D Data
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2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE International Conference on Computer Vision (ICCV) ECCV 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE International Conference on Computer Vision (ICCV) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2018 International Conference on 3D Vision (3DV) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Image Vis. Comput. ICST MICCAI (1) WorldCIST (3) BMVC SiPS 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE International Conference on Computer Vision (ICCV) 2018 International Conference on 3D Vision (3DV) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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