Experiments have been conducted to demonstrate the validity and robustness of the proposed technique . It is capable of satisfying various 3D shape reconstruction demands in scientific research and engineering applications . 3D shape reconstruction from a single image is a highly ill-posed problem . Modern deep learning based systems try to solve this problem by learning an end-to-end mapping from image to shape via a deep network . Three-dimensional ( 3D ) shape reconstruction is a fundamental problem in machine vision applications . Shape From Focus ( SFF ) is one of the passive optical methods for 3D shape recovery that uses degree of focus as a cue to estimate 3D shape . Three‐dimensional ( 3D ) shape reconstruction from 2D image data has been receiving extensive attention in medical engineering . It can enhance accurate diagnosis of the disease from medical images of computer tomography ( CT ) and magnetic resonance imaging ( MRI ) . Control points and weights are treated as the decision variables in the optimization process . The 3D shape reconstruction problem is reduced to a problem that comprises stereo reconstruction of control points and computation of the corresponding weights . The correspondence between the control points in the two images is obtained using a third image . Using the homotopy equivalence , we also show that the reconstructed object is homeomorphic ( and isotopic ) to the original object . This is the first time that 3-dimensional shape reconstruction from cross-sections comes with theoretical guarantees . For 3D full-shape reconstruction , we develop a measuring system that consists of a range sensor and a motorized stage , where the motorized stage can be controlled through a computer . We then propose a calibration method for estimating the transformation between the coordinate system of the range sensor and that of the motorized stage . Region-based Segmentation on Evolving Surfaces with Application to 3D Reconstruction of Shape and Piecewise Constant Radiance Hailin Jin 1 , Anthony J. Yezzi 2 , and Stefano Soatto 1 Computer Science Department , University of California , Los Angeles , CA 90095 , hljin,soatto@cs.ucla.edu School of Electrical and Computer Engineering , Georgia Institute of Technology , Atlanta , GA 30332 , ayezzi@ece.gatech.edu Abstract . We consider the problem of estimating the shape and radi- ance of a scene from a calibrated set of images under the assumption that the scene is Lambertian and its radiance is piecewise constant . Pose calibration is widely used in the fields of 3D ( three-dimensional ) shape reconstruction , vision-based robot navigation , augmented reality , and so on . However , if two cameras have the non-overlapped FOV ( Field Of View ) , existing methods can not calibrate their relative position because the calibration target is invisible to one camera . 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 . Our method offers strong robustness and good performance even in presence of large poses ( the 3th , 4th and 5th columns ) and occlusions ( the 6th , 7th and 8th columns ) . This results in a more powerful model : a single - pass encoder , which is learnt jointly with the model , achieves state - of - the - art face alignment performance on AFLW2000 benchmark dataset . 3D Face Reconstruction . 3DMM also demonstrates its strength in face reconstruction . A moving area detection method is then proposed to reconstruct the adaptively segmented moving areas into a series of video frames , which are filled back into the stationary clear background . Both simulation and experimental results demonstrate that the proposed method significantly reduces the time consumption of video reconstruction without degradation of image quality . The Eulerian method , which has been widely employed in flow simulation , stores variables such as velocity and density on regular Cartesian grids , thereby it could be associated with ( volumetric ) video data on the same domain . This paper proposes a novel method for flow simulation , which is tightly coupling video-based reconstruction with physically-based simulation and making use of meaningful physical attributes during re-simulation . First , we reconstruct the density field from a single-view video . We also present an approximate alternative formulation that selects and validates only one hypothesis based on confidence . Both formulations enable us to perform video-based reconstruction at up to 25 frames per second . We show results on the multi-view stereo evaluation benchmark datasets and several outdoors video sequences . We present the 3DVC framework and describe the methods used to construct incrementally the object models and the pose estimates from unregistered noisy depth and texture measurements . We also describe a method for video frame reconstruction based on 3-D scene assembly , and discuss potential applications of 3DVC to video coding and content-based handling . 3DVC assumes that the objects in the scene are rigid and segmented . Abstract The prior knowledge of signal is the previous condition of image compressed sensing reconstruction . In order to improve the quality of the priors except for image sparsity , this paper proposes a new model of video image reconstruction . The texture is the important visual feature of video image as a result of its repeat , leading to image global geometrical structures . During the compression of image , to get a high compressed ratio , people always only get a degraded image . This article aims to estimate the degraded model of the video compresed image through using the neural network and H.264 rate-distortion model.we can improve the quality of the compressed image , using neural network 's predictive ability strongly and high fault-tolerance , improving the quantization parameter prediction in the video encoding , completing video image reconstruction . The experiments show that the neural network can improve the image quality of video compressed image greatly . The video transmission via Wireless Multimedia Sensor Networks ( WMSNs ) needs high rate which motivated the use of Ultra Wide Band ( UWB ) Audio Visual ( AV ) PHY IEEE 802.15.3c . This paper proposes a robust video transmission of H.264/AVC baseline coding over UWB AV PHY IEEE 802.15.3c using Unequal Error Protection ( UEP ) , Hierarchical Modulation and Adaptive modulation , which takes into consideration the importance of the different frames in the Group Of Pictures ( GOP ) and the sensitivity of the encoded bit stream to the transmission errors caused by the UWB wireless channel to improve the video quality reconstruction . Simulation results show that the proposed UEP scheme outperforms the Equal Error Protection ( EEP ) scheme by up to 15dB in terms of Average Peak Signal-to-noise Ratio ( Average PSNR ) . Video quality retrieval is an important issue in reconstruction video sequences . The amounts of transmission bitstreams are very large to achieve high video quality reconstruction . Thus , high video quality retrieval is still a challenge problem . In this work , we consider a video system where acquisition is performed via framewise pure compressed sensing . The burden of quality video sequence reconstruction falls , then , solely on the decoder side . We show that effective decoding can be carried out at the receiver/decoder side in the form of interframe total variation minimization . Abstract This paper presents a framework for ‘ Filling In ’ missing gaps in images and particularly patches with texture . The algorithm can also be used as a fallback mode in treating missing data for video sequence reconstruction . The underlying idea is to construct a parametric model of the p.d.f . The infimal convolution of total ( generalized ) variation -- type functionals and its application as regularization for video and image reconstruction is considered . The definition of this particular type of regularization functional is motivated by the need of suitably combining spatial and temporal regularity requirements for video processing . Both of these novel approaches make extensive use of Kronecker matrix algebra to design massively scalable algorithms which have low memory requirements . We demonstrate exact GP inference for a spatial-temporal climate modelling problem with 3.7 million training points as well as a video reconstruction problem with 1 billion points . A New Benchmark Dataset and Classification Model on Real-World Data . Deep learning techniques for point cloud data have demonstrated great potentials in solving classical problems in 3D computer vision such as 3D object classification and segmentation . Several recent 3D object classification methods have reported state-of-the-art performance on CAD model datasets such as ModelNet40 with high accuracy ( ~92\% ) . Two well-known datasets , ModelNet40 shape classification benchmark and Stanford 3D Indoor Semantics Dataset , are used to test the performance of the proposed method . Compared with other methods with complicated structures , the proposed method with only a simple deep learning network , can achieve a higher accuracy in 3D object classification and semantic segmentation . Our approach is based on extracting and aggregating only the essential characteristics of an object class for a task . We show how the proposed method significantly improves on previous work in 3D object categorization . We discuss the benefits of the method in other scenarios such as robot grasping . This paper presents a novel approach for 3D object recognition and classification . The originality of this approach is the combination of statistical reasoning , feature learning and expertise knowledge from the application . Abstract Point cloud processing has gained consideration for 3D object recognition and classification tasks . In this context , an important task is to detect the distinct and repeatable 3D keypoints . In this paper we present a technique for constructing kernels on the measured data directly without explicit phase unwrapping . Such a kernel will naturally incorporate the 3D depth information and can be used to improve the systems involving 3D object analysis and classification . It avoids possible phase unwrapping errors introduced during object reconstruction . Biomedical applications of radial basis function networks.- 8 . 3-D visual object classification with hierarchical radial basis function networks.- 9 . Controller applications using radial basis function networks.- 10 . The proposed Boosting Constrained Mutual Subspace Method(BCMSM ) improves the original CMSM in the following ways : a ) The proposed BCMSM algorithm is insensitive to the dimension of the generalized differnce subspace while the performance of the original CMSM algorithm is quite dependent on the dimension and the selecting of optimum choice is quite empirical and case-dependent ; b ) By taking advantage of both boosting and CMSM techniques , the generalization ability is improved and much higher classification performance can be achieved . Extensive experiments on real-life data sets ( two face recognition tasks and one 3D object category classification task ) show that the proposed method outperforms the previous state-of-the-art algorithms greatly in terms of classification accuracy . Semantic learning on 3D point clouds model using a deep network has received great interests to address the 3D object classification problem . The complete information of the 3D object is described by the disordered data structure . 3D shape feature learning plays a pivotal role in both industry and academia . PointCNN is one of excellent neural networks for 3D object databases classification . Instead of selecting representative points arbitrarily in PointCNN , clustering-enhanced PointCNN proposed in this paper can make representative points more logical and efficient for point cloud classification learning . A preliminary version of this manuscript was published previously . Since then , the frameworks of RPN and Faster R - CNN have been adopted and generalized to other methods , such as 3D object detection , part - based detection , instance segmentation , and image captioning . Our fast and effective object detection system has also been built in commercial systems such as at Pinterests , with user engagement improvements reported . document : Frustum PointNets for 3D Object Detection from RGB - D Data In this work , we study 3D object detection from RGB - D data in both indoor and outdoor scenes . section : Abstract We introduce a novel method for 3D object detection and pose estimation from color images only . We first use segmentation to detect the objects of interest in 2D even in presence of partial occlusions and cluttered background . The objects are centrally placed on randomly chosen backgrounds , and there is also cluttering from a peripherally placed second object . This database is designed for experimenting with 3D object recognition from shape . It contains images of 50 toys belonging to 5 generic categories : four - legged animals , human figures , airplanes , trucks , and cars . subsection : Input 3D Fields We study the 3D shape classification problem by employing a deep neural network . The input of our network is a 3D vector field built from the input shape and the output is an object category label . Object classification is widely used in computer vision as a testbed for evaluating neural network designs , and the neural network parameters learned from this task may be transferred to other high - level understanding tasks such as object retrieval and scene parsing . Thus we choose 3D object classification as the task for evaluating our FPNN . section : Results and Discussions training / testing split with 9 , 843 / 2 , 468 models ) — the standard benchmark for 3D object classification task , in our experiments . Models in this dataset are already aligned with a canonical orientation . More sophisticated models introduce occlusion priors or semantic information . For rigid body pose estimation and 3D object analysis , several descriptors have been proposed . Given the success of SIFT , there have been several attempts at embedding rotational and translational invariance . In this paper , we present a novel method for real-time 3D hand pose estimation from single depth images using 3D Convolutional Neural Networks ( CNNs ) . Image-based features extracted by 2D CNNs are not directly suitable for 3D hand pose estimation due to the lack of 3D spatial information . In recent years , we have witnessed a steady growth of the research in real-time 3D hand pose estimation with depth cameras , since this technology plays an important role in various human – computer interaction applications . In this chapter , we first review existing techniques and systems for real-time 3D hand pose estimation . Finally , the 3D surface is formed by recombination of the edges in a 2D image sequence . Experiments on water bubble ultrasonic images show that the new method is successful in detection of 3D surfaces . In this paper the analysis of three-dimensional ( 3D ) medical images is investigated . An algorithm for the detection of 3D surfaces from MR row data ( in the frequency domain ) is implemented on a fine-grained , highly parallel SIMD computer , the Connection Machine . The surface reconstruction is performed using a 3D FFT algorithm , while a ray tracing algoritiini is is,:d for the volumetric rendering of the output . Then we propose three conditions to check the inconsistency of the inequality system , which geometrically indicates whether the convex region formed by the inequality system is empty . We have implemented the algorithms and confirmed their real-time efficiency for the synthesis of 3D form-closure grasps . Stereo video coding and multi-view video plus depth coding method are also included in this system . This system can be applied to 3D video conferences , 3D entertainment and 3D security monitoring , etc . An optical encryption of three-dimensional ( 3D ) object with digital holography was implemented . In the process of encryption , two holograms involved recording key information and 3D object were obtained . The effectiveness of the proposed approach for meshes with a large range of coarseness is demonstrated by experiments . The application of the proposed method to 3D surface segmentation and 3D mesh feature extraction is also discussed . In this paper we present an uncertainty-driven viewpoint planning approach for measurement and digitalization of free form 3D objects . The object surface is first decomposed into a number of cross section curves , and each curve is reconstructed by a closed B-spline curve . This paper proposes a 3D real-time video-teleconferencing system over IP broadband networks . The video capturing uses two synchronized cameras positioned side by side in front of the person who will communicate , in order to compute his or her three dimensional information to be transmitted to the remote side . ( Estimation de Mouvement 3D et Evaluation dans des Séquences Volumiques de Microscopie de Fluorescence ) . The thesis work deals with the computation and the assessment of 3D motion fields in 3D fluorescence microscopy image sequences . Rapid image matching is the key to real-time 3D information acquisition , and parallel processing technology is an important way for solving the problem of computing speed . Image matching algorithm based on probability relaxation was firstly analyzed . This is inspired by the observation that a large number of 2D face datasets [ reference ][ reference ][ reference ][ reference ][ reference ] are available with obtainable 2D landmark annotations , that could provide valuable information for 3D model learning , without requiring new data with 3D annotations . Since these 2D images do not have any 3D annotations , it is not straightforward to exploit them in 3D face model learning . We design a novel self - supervised learning method that is able to train a 3D face model with weak supervision from 2D images . Models in this dataset are already aligned with a canonical orientation . For 3D object recognition scenarios in real world , the gravity direction can often be captured by the sensor , but the horizontal ‘ ‘ facing ’ ’ direction of the objects are unknown . We augment ModelNet40 data by randomly rotating the shapes horizontally . section : Conclusion We introduce a novel neural network architecture , the Synchronized Spectral CNN ( SyncSpecCNN ) , for semantic annotation on 3D shape graphs . To share coefficients and conduct multi - scale analysis in different parts of a single shape graph , we introduce a spectral parametrization of dilated convolutional kernels . A paired sample t - test showed that the difference between our approach and the other best ranked method in the challenge ( i.e. , MSL_SKKU ) is not statistically significant . Although various CNN - based networks have been used successfully for medical image segmentation ( e.g. , 3D U - Net or DenseVoxNet ) , an important benefit of our architecture is the reduced number of trainable parameters . While U - Net and DenseVoxNet require nearly 19 M and 4 M of parameters , respectively , our semidense network has less than 1 M of parameters ( nearly 900 , 000 ) , which results in a 75 - 90 lighter model . To handle models consisting of a mixture of Gaussians , we follow and simply solve for each Gaussian independently and select the most probably solution . subsection : Projecting 3D poses onto 2D belief maps The projected pose model is interleaved throughout the architecture ( see Figure [ reference ] ) . To this end , we explore two novel convolution operators : sparse convolution ( SC ) and valid sparse convolution ( VSC ) . In our experiments with recognizing handwritten digits and 3D shapes , networks using SC and VSC achieve state - of - the - art performance whilst reducing the computation and memory requirements by . section : Motivation
Experiments have been conducted to demonstrate the validity and robustness of the proposed technique . It is capable of satisfying various 3D shape reconstruction demands in scientific research and engineering applications .
3D shape reconstruction from a single image is a highly ill-posed problem . Modern deep learning based systems try to solve this problem by learning an end-to-end mapping from image to shape via a deep network .
Three-dimensional ( 3D ) shape reconstruction is a fundamental problem in machine vision applications . Shape From Focus ( SFF ) is one of the passive optical methods for 3D shape recovery that uses degree of focus as a cue to estimate 3D shape .
Three‐dimensional ( 3D ) shape reconstruction from 2D image data has been receiving extensive attention in medical engineering . It can enhance accurate diagnosis of the disease from medical images of computer tomography ( CT ) and magnetic resonance imaging ( MRI ) .
Control points and weights are treated as the decision variables in the optimization process . The 3D shape reconstruction problem is reduced to a problem that comprises stereo reconstruction of control points and computation of the corresponding weights . The correspondence between the control points in the two images is obtained using a third image .
Using the homotopy equivalence , we also show that the reconstructed object is homeomorphic ( and isotopic ) to the original object . This is the first time that 3-dimensional shape reconstruction from cross-sections comes with theoretical guarantees .
For 3D full-shape reconstruction , we develop a measuring system that consists of a range sensor and a motorized stage , where the motorized stage can be controlled through a computer . We then propose a calibration method for estimating the transformation between the coordinate system of the range sensor and that of the motorized stage .
Region-based Segmentation on Evolving Surfaces with Application to 3D Reconstruction of Shape and Piecewise Constant Radiance Hailin Jin 1 , Anthony J. Yezzi 2 , and Stefano Soatto 1 Computer Science Department , University of California , Los Angeles , CA 90095 , hljin,soatto@cs.ucla.edu School of Electrical and Computer Engineering , Georgia Institute of Technology , Atlanta , GA 30332 , ayezzi@ece.gatech.edu Abstract . We consider the problem of estimating the shape and radi- ance of a scene from a calibrated set of images under the assumption that the scene is Lambertian and its radiance is piecewise constant .
Pose calibration is widely used in the fields of 3D ( three-dimensional ) shape reconstruction , vision-based robot navigation , augmented reality , and so on . However , if two cameras have the non-overlapped FOV ( Field Of View ) , existing methods can not calibrate their relative position because the calibration target is invisible to one camera .
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 . Our method offers strong robustness and good performance even in presence of large poses ( the 3th , 4th and 5th columns ) and occlusions ( the 6th , 7th and 8th columns ) .
This results in a more powerful model : a single - pass encoder , which is learnt jointly with the model , achieves state - of - the - art face alignment performance on AFLW2000 benchmark dataset . 3D Face Reconstruction . 3DMM also demonstrates its strength in face reconstruction .
A moving area detection method is then proposed to reconstruct the adaptively segmented moving areas into a series of video frames , which are filled back into the stationary clear background . Both simulation and experimental results demonstrate that the proposed method significantly reduces the time consumption of video reconstruction without degradation of image quality .
The Eulerian method , which has been widely employed in flow simulation , stores variables such as velocity and density on regular Cartesian grids , thereby it could be associated with ( volumetric ) video data on the same domain . This paper proposes a novel method for flow simulation , which is tightly coupling video-based reconstruction with physically-based simulation and making use of meaningful physical attributes during re-simulation . First , we reconstruct the density field from a single-view video .
We also present an approximate alternative formulation that selects and validates only one hypothesis based on confidence . Both formulations enable us to perform video-based reconstruction at up to 25 frames per second . We show results on the multi-view stereo evaluation benchmark datasets and several outdoors video sequences .
We present the 3DVC framework and describe the methods used to construct incrementally the object models and the pose estimates from unregistered noisy depth and texture measurements . We also describe a method for video frame reconstruction based on 3-D scene assembly , and discuss potential applications of 3DVC to video coding and content-based handling . 3DVC assumes that the objects in the scene are rigid and segmented .
Abstract The prior knowledge of signal is the previous condition of image compressed sensing reconstruction . In order to improve the quality of the priors except for image sparsity , this paper proposes a new model of video image reconstruction . The texture is the important visual feature of video image as a result of its repeat , leading to image global geometrical structures .
During the compression of image , to get a high compressed ratio , people always only get a degraded image . This article aims to estimate the degraded model of the video compresed image through using the neural network and H.264 rate-distortion model.we can improve the quality of the compressed image , using neural network 's predictive ability strongly and high fault-tolerance , improving the quantization parameter prediction in the video encoding , completing video image reconstruction . The experiments show that the neural network can improve the image quality of video compressed image greatly .
The video transmission via Wireless Multimedia Sensor Networks ( WMSNs ) needs high rate which motivated the use of Ultra Wide Band ( UWB ) Audio Visual ( AV ) PHY IEEE 802.15.3c . This paper proposes a robust video transmission of H.264/AVC baseline coding over UWB AV PHY IEEE 802.15.3c using Unequal Error Protection ( UEP ) , Hierarchical Modulation and Adaptive modulation , which takes into consideration the importance of the different frames in the Group Of Pictures ( GOP ) and the sensitivity of the encoded bit stream to the transmission errors caused by the UWB wireless channel to improve the video quality reconstruction . Simulation results show that the proposed UEP scheme outperforms the Equal Error Protection ( EEP ) scheme by up to 15dB in terms of Average Peak Signal-to-noise Ratio ( Average PSNR ) .
Video quality retrieval is an important issue in reconstruction video sequences . The amounts of transmission bitstreams are very large to achieve high video quality reconstruction . Thus , high video quality retrieval is still a challenge problem .
In this work , we consider a video system where acquisition is performed via framewise pure compressed sensing . The burden of quality video sequence reconstruction falls , then , solely on the decoder side . We show that effective decoding can be carried out at the receiver/decoder side in the form of interframe total variation minimization .
Abstract This paper presents a framework for ‘ Filling In ’ missing gaps in images and particularly patches with texture . The algorithm can also be used as a fallback mode in treating missing data for video sequence reconstruction . The underlying idea is to construct a parametric model of the p.d.f .
The infimal convolution of total ( generalized ) variation -- type functionals and its application as regularization for video and image reconstruction is considered . The definition of this particular type of regularization functional is motivated by the need of suitably combining spatial and temporal regularity requirements for video processing .
Both of these novel approaches make extensive use of Kronecker matrix algebra to design massively scalable algorithms which have low memory requirements . We demonstrate exact GP inference for a spatial-temporal climate modelling problem with 3.7 million training points as well as a video reconstruction problem with 1 billion points .
A New Benchmark Dataset and Classification Model on Real-World Data . Deep learning techniques for point cloud data have demonstrated great potentials in solving classical problems in 3D computer vision such as 3D object classification and segmentation . Several recent 3D object classification methods have reported state-of-the-art performance on CAD model datasets such as ModelNet40 with high accuracy ( ~92\% ) .
Two well-known datasets , ModelNet40 shape classification benchmark and Stanford 3D Indoor Semantics Dataset , are used to test the performance of the proposed method . Compared with other methods with complicated structures , the proposed method with only a simple deep learning network , can achieve a higher accuracy in 3D object classification and semantic segmentation .
Our approach is based on extracting and aggregating only the essential characteristics of an object class for a task . We show how the proposed method significantly improves on previous work in 3D object categorization . We discuss the benefits of the method in other scenarios such as robot grasping .
This paper presents a novel approach for 3D object recognition and classification . The originality of this approach is the combination of statistical reasoning , feature learning and expertise knowledge from the application .
Abstract Point cloud processing has gained consideration for 3D object recognition and classification tasks . In this context , an important task is to detect the distinct and repeatable 3D keypoints .
In this paper we present a technique for constructing kernels on the measured data directly without explicit phase unwrapping . Such a kernel will naturally incorporate the 3D depth information and can be used to improve the systems involving 3D object analysis and classification . It avoids possible phase unwrapping errors introduced during object reconstruction .
Biomedical applications of radial basis function networks.- 8 . 3-D visual object classification with hierarchical radial basis function networks.- 9 . Controller applications using radial basis function networks.- 10 .
The proposed Boosting Constrained Mutual Subspace Method(BCMSM ) improves the original CMSM in the following ways : a ) The proposed BCMSM algorithm is insensitive to the dimension of the generalized differnce subspace while the performance of the original CMSM algorithm is quite dependent on the dimension and the selecting of optimum choice is quite empirical and case-dependent ; b ) By taking advantage of both boosting and CMSM techniques , the generalization ability is improved and much higher classification performance can be achieved . Extensive experiments on real-life data sets ( two face recognition tasks and one 3D object category classification task ) show that the proposed method outperforms the previous state-of-the-art algorithms greatly in terms of classification accuracy .
Semantic learning on 3D point clouds model using a deep network has received great interests to address the 3D object classification problem . The complete information of the 3D object is described by the disordered data structure .
3D shape feature learning plays a pivotal role in both industry and academia . PointCNN is one of excellent neural networks for 3D object databases classification . Instead of selecting representative points arbitrarily in PointCNN , clustering-enhanced PointCNN proposed in this paper can make representative points more logical and efficient for point cloud classification learning .
A preliminary version of this manuscript was published previously . Since then , the frameworks of RPN and Faster R - CNN have been adopted and generalized to other methods , such as 3D object detection , part - based detection , instance segmentation , and image captioning . Our fast and effective object detection system has also been built in commercial systems such as at Pinterests , with user engagement improvements reported .
document : Frustum PointNets for 3D Object Detection from RGB - D Data In this work , we study 3D object detection from RGB - D data in both indoor and outdoor scenes .
section : Abstract We introduce a novel method for 3D object detection and pose estimation from color images only . We first use segmentation to detect the objects of interest in 2D even in presence of partial occlusions and cluttered background .
The objects are centrally placed on randomly chosen backgrounds , and there is also cluttering from a peripherally placed second object . This database is designed for experimenting with 3D object recognition from shape . It contains images of 50 toys belonging to 5 generic categories : four - legged animals , human figures , airplanes , trucks , and cars .
subsection : Input 3D Fields We study the 3D shape classification problem by employing a deep neural network . The input of our network is a 3D vector field built from the input shape and the output is an object category label .
Object classification is widely used in computer vision as a testbed for evaluating neural network designs , and the neural network parameters learned from this task may be transferred to other high - level understanding tasks such as object retrieval and scene parsing . Thus we choose 3D object classification as the task for evaluating our FPNN . section : Results and Discussions
training / testing split with 9 , 843 / 2 , 468 models ) — the standard benchmark for 3D object classification task , in our experiments . Models in this dataset are already aligned with a canonical orientation .
More sophisticated models introduce occlusion priors or semantic information . For rigid body pose estimation and 3D object analysis , several descriptors have been proposed . Given the success of SIFT , there have been several attempts at embedding rotational and translational invariance .
In this paper , we present a novel method for real-time 3D hand pose estimation from single depth images using 3D Convolutional Neural Networks ( CNNs ) . Image-based features extracted by 2D CNNs are not directly suitable for 3D hand pose estimation due to the lack of 3D spatial information .
In recent years , we have witnessed a steady growth of the research in real-time 3D hand pose estimation with depth cameras , since this technology plays an important role in various human – computer interaction applications . In this chapter , we first review existing techniques and systems for real-time 3D hand pose estimation .
Finally , the 3D surface is formed by recombination of the edges in a 2D image sequence . Experiments on water bubble ultrasonic images show that the new method is successful in detection of 3D surfaces .
In this paper the analysis of three-dimensional ( 3D ) medical images is investigated . An algorithm for the detection of 3D surfaces from MR row data ( in the frequency domain ) is implemented on a fine-grained , highly parallel SIMD computer , the Connection Machine . The surface reconstruction is performed using a 3D FFT algorithm , while a ray tracing algoritiini is is,:d for the volumetric rendering of the output .
Then we propose three conditions to check the inconsistency of the inequality system , which geometrically indicates whether the convex region formed by the inequality system is empty . We have implemented the algorithms and confirmed their real-time efficiency for the synthesis of 3D form-closure grasps .
Stereo video coding and multi-view video plus depth coding method are also included in this system . This system can be applied to 3D video conferences , 3D entertainment and 3D security monitoring , etc .
An optical encryption of three-dimensional ( 3D ) object with digital holography was implemented . In the process of encryption , two holograms involved recording key information and 3D object were obtained .
The effectiveness of the proposed approach for meshes with a large range of coarseness is demonstrated by experiments . The application of the proposed method to 3D surface segmentation and 3D mesh feature extraction is also discussed .
In this paper we present an uncertainty-driven viewpoint planning approach for measurement and digitalization of free form 3D objects . The object surface is first decomposed into a number of cross section curves , and each curve is reconstructed by a closed B-spline curve .
This paper proposes a 3D real-time video-teleconferencing system over IP broadband networks . The video capturing uses two synchronized cameras positioned side by side in front of the person who will communicate , in order to compute his or her three dimensional information to be transmitted to the remote side .
( Estimation de Mouvement 3D et Evaluation dans des Séquences Volumiques de Microscopie de Fluorescence ) . The thesis work deals with the computation and the assessment of 3D motion fields in 3D fluorescence microscopy image sequences .
Rapid image matching is the key to real-time 3D information acquisition , and parallel processing technology is an important way for solving the problem of computing speed . Image matching algorithm based on probability relaxation was firstly analyzed .
This is inspired by the observation that a large number of 2D face datasets [ reference ][ reference ][ reference ][ reference ][ reference ] are available with obtainable 2D landmark annotations , that could provide valuable information for 3D model learning , without requiring new data with 3D annotations . Since these 2D images do not have any 3D annotations , it is not straightforward to exploit them in 3D face model learning . We design a novel self - supervised learning method that is able to train a 3D face model with weak supervision from 2D images .
Models in this dataset are already aligned with a canonical orientation . For 3D object recognition scenarios in real world , the gravity direction can often be captured by the sensor , but the horizontal ‘ ‘ facing ’ ’ direction of the objects are unknown . We augment ModelNet40 data by randomly rotating the shapes horizontally .
section : Conclusion We introduce a novel neural network architecture , the Synchronized Spectral CNN ( SyncSpecCNN ) , for semantic annotation on 3D shape graphs . To share coefficients and conduct multi - scale analysis in different parts of a single shape graph , we introduce a spectral parametrization of dilated convolutional kernels .
A paired sample t - test showed that the difference between our approach and the other best ranked method in the challenge ( i.e. , MSL_SKKU ) is not statistically significant . Although various CNN - based networks have been used successfully for medical image segmentation ( e.g. , 3D U - Net or DenseVoxNet ) , an important benefit of our architecture is the reduced number of trainable parameters . While U - Net and DenseVoxNet require nearly 19 M and 4 M of parameters , respectively , our semidense network has less than 1 M of parameters ( nearly 900 , 000 ) , which results in a 75 - 90 lighter model .
To handle models consisting of a mixture of Gaussians , we follow and simply solve for each Gaussian independently and select the most probably solution . subsection : Projecting 3D poses onto 2D belief maps The projected pose model is interleaved throughout the architecture ( see Figure [ reference ] ) .
To this end , we explore two novel convolution operators : sparse convolution ( SC ) and valid sparse convolution ( VSC ) . In our experiments with recognizing handwritten digits and 3D shapes , networks using SC and VSC achieve state - of - the - art performance whilst reducing the computation and memory requirements by . section : Motivation
Single-shot 3D shape reconstruction using deep convolutional neural networks Single-view Object Shape Reconstruction Using Deep Shape Prior and Silhouette Optimal depth estimation by combining focus measures using genetic programming Three‐dimensional CAD model reconstruction from image data of computer tomography NURBS-based geometric inverse reconstruction of free-form shapes Geometric Tomography with Topological Guarantees Calibration of a System with a Range Sensor and a Motorized Stage for 3D Full - Shape Reconstruction Region-based segmenta-tion on evolving surfaces with application to 3D shape and radiance estimation Pose calibration of two cameras with non-overlapped field of view Joint 3D Face Reconstruction and Dense Face Alignment from A Single Image with 2D-Assisted Self-Supervised Learning Nonlinear 3D Face Morphable Model Adaptive reconstruction for coded aperture temporal compressive imaging. Augmented Flow Simulation Based on Tight Coupling Between Video Reconstruction and Eulerian Models Real-Time Visibility-Based Fusion of Depth Maps Video representation with three-dimensional entities Nonlocal regularization based on DT-CWT for WMSN video image iterative reconstruction Application of Artificial Neural Network in Video Compression Coding H.264/AVC Video Transmission over UWB AV PHY IEEE 802.15.3c using UEP and Adaptive Modulation Techniques A SCALABLE BLOCK COMPENSATION WITH INDEX SELECTION MECHANISM TOACHIEVE HIGH VIDEO QUALITY RETRIEVAL Decoding of framewise compressed-sensed video via interframe total variation minimization A statistical framework for picture reconstruction using 2D AR models On Infimal Convolution of TV-Type Functionals and Applications to Video and Image Reconstruction Exploiting Structure for Fast Kernel Learning Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data A Novel Point Cloud Encoding Method Based on Local Information for 3D Classification and Segmentation Extracting essential local object characteristics for 3D object categorization Object recognition from 3D blurred images Fuzzy logic and histogram of normal orientation-based 3D keypoint detection for point clouds Wrapped phase based SVM method for 3D object recognition New advances in design Boosting Constrained Mutual Subspace Method for Robust Image-Set Based Object Recognition A Neural Network for Point Clouds Classification Clustering-enhanced PointCNN for Point Cloud Classification Learning Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Frustum PointNets for 3D Object Detection from RGB-D Data BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth Multi-column deep neural networks for image classification FPNN: Field Probing Neural Networks for 3D Data Towards Viewpoint Invariant 3D Human Pose Estimation Real-Time 3D Hand Pose Estimation with 3D Convolutional Neural Networks Real-Time Hand Pose Estimation Using Depth Camera An automatic 3D surface extraction in ultrasonic images 3D Medical Imaging On The Connection Machine The synthesis of 3D form-closure grasps Designing and Realization of 3D Video Capture and Transfer System Based on Kinect A Robust Optical Encryption Method for Three-Dimensional Object Based on the Fresnel Transform Estimating Gaussian curvatures from 3D meshes Uncertainty-driven viewpoint planning for 3D object measurements A System for 3D Teleconference over IP Network 3D Motion Estimation and Assessment in Fluorescence Microscopy Volume Sequences. (Estimation de Mouvement 3D et Evaluation dans des Séquences Volumiques de Microscopie de Fluorescence) Parallel Image Matching Algorithm in a Cluster Environment Joint 3D Face Reconstruction and Dense Face Alignment from A Single Image with 2D-Assisted Self-Supervised Learning FPNN: Field Probing Neural Networks for 3D Data SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image Submanifold Sparse Convolutional Networks
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ArXiv 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ICCV CommNet SDM IROS ICPR (1) IJCAI IJCNN IEEE Transactions on Pattern Analysis and Machine Intelligence 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE International Conference on Computer Vision (ICCV) 2012 IEEE Conference on Computer Vision and Pattern Recognition NIPS ECCV IIH-MSP Human Vision and Electronic Imaging ICRA SVR ArXiv NIPS 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Comput. Medical Imaging Graph. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv
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