We present here a framework for coupled action recognition and pose estimation by formulating pose estimation as an optimization over a set of action-specific manifolds . The framework allows for integration of a 2D appearance-based action recognition system as a prior for 3D pose estimation and for refinement of the action labels using relational pose features based on the extracted 3D poses . Our experiments show that our pose estimation system is able to estimate body poses with high degrees of freedom using very few particles and can achieve state-of-the-art results on the HumanEva-II benchmark . PLSA has been successfully used to model the co-occurrence of dyadic data on problems such as image annotation where image features are mapped to word categories via latent variable semantics . We apply the PLSA approach to motion tracking by extending it to a sequential setting where the latent variables describe intrinsic motion semantics linking human figure appearance to 3D pose estimates . This approach is in contrast to many current methods that directly learn the often high-dimensional image-to-pose mappings and utilize subspace projections as a constraint on the pose space alone . Experiments demonstrate that the proposed algorithm tracks 3D body pose efficiently and accurately , even when self-occlusion , motion blur and large limb movements occur . Quantitative comparisons show that the formulation produces more accurate 3D pose estimates over time than those that can be obtained via a number of previously-proposed particle filtering based tracking algorithms . The proposed approach employs point correspondences and assumes that the human eye has a spherical shape . The image registration problem is formulated as a 3D pose estimation problem , solved by estimating the rigid transformation that relates the views from which the two images were acquired . Given this estimate , each image can be warped upon the other so that pixels with the same coordinates image the same retinal point . Our DDL approach aims at simultaneously constructing two overcomplete dictionaries , called the visual observation dictionary ( VBD ) and the body configuration dictionary ( BCD ) , with a shared sparse representation ( SSR ) regularization with respect to every data sample . Under such regularization , two dictionaries are tied together and the 3D pose estimation problem can be reduced to a simple ℓ1 optimization problem given a new test visual observation . We also propose a efficient algorithm based on inexact Augmented Lagrange Multiplier ( IALM ) method to solve the above DDL optimization model . We demonstrate that pose estimation from the corresponding feature points can be formed as a solution to Sylvester 's equation . We show that the proposed approach to the solution of Sylvester 's equation is equivalent to the classical SVD method for 3D-3D pose estimation . However , whereas classical SVD can not be used for pose estimation directly from 2-D image sequences , our method based on Sylvester 's equation provides a new approach to pose estimation . We demonstrate that the classical Singular Value Decomposition ( SVD ) approach to pose estimation provides a solution to Sylvester 's equation in 3D-3D pose estimation . The proposed approach to the solution of Sylvester 's equation is therefore equivalent to the classical SVD method for 3D-3D pose estimation , yet it can also be used for pose estimation from 2D image sequences . Finally , we rely on computer simulation experiments to demonstrate the performance of our algorithm on video sequences . It has the advantage of evaluating related algorithms using non-dimensional metrics for fixed and continuous intervals . In addition , the proposed methodology is validated using a proof of concept scenario based on the 3D pose estimation phase in the 3D reconstruction pipeline . The obtained results are very promising and emphasize the methodology 's generic nature , clearing the way for its application in a multitude of scenarios , such as computer vision and 3D reconstruction . In this position paper we propose the use of the Distributional Clauses Particle Filter in conjunction with a model-based 3D object tracking method in monocular camera sequences . We describe the model based object tracking method that is based on contour and edge features for 3D pose relative estimation . We also describe the application of the Distributional Clauses Particle Filter that takes into account inputs from object tracking . 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 . Finding the correct 3D pose that matches the image requires injecting additional information usually in the form of 3D geometric pose priors and temporal or structural constraints . We propose a new joint approach to 2D landmark detection and full 3D pose estimation from a single RGB image that takes advantage of reasoning jointly about the estimation of 2D and 3D landmark locations to improve both tasks . We propose a novel CNN architecture that learns to combine the image appearance based predictions provided by convolutional - pose - machine style 2D landmark detectors , with the geometric 3D skeletal information encoded in a novel pretrained model of 3D human pose . Another recent example is Bogo , who fit a detailed statistical 3D body model to 2D joint proposals . Zhou tackles the problem of 3D pose estimation for a monocular image sequence integrating 2D , 3D and temporal information to account for uncertainties in the model and the measurements . Similar to our proposed approach , Zhou ’s method does not need synchronized 2D - 3D training data , it only needs 2D pose annotations to train the CNN joint regressor and a separate 3D mocap dataset to learn the 3D sparse basis . • We firstly cast the problem of estimating 3D pose from a single depth map into a voxel - to - voxel prediction . Unlike most of the previous methods that regress 3D coordinates directly from the 2D depth image , our proposed V2V - PoseNet estimates the per - voxel likelihood from a voxelized grid input . It also allows us to win the 3D hand pose estimation challenge . As voxel - tovoxel prediction is firstly tried in 3D hand and human pose estimation from a single depth map , we hope this work to provide a new way of accurate 3D pose estimation . section : We design a software which applies our algorithm to various datasets . Experiments on 3D point clouds , mineral structures , and images show the effectiveness of our algorithm in practice . The second mobility aspect is related with a novel Software as a Service system that allows access to robotic functionalities and data over the Ethernet , thus we demonstrate the use of the novel NVIDIA GRID technology allowing to virtualize the graphic processing unit . We introduce Complex Shape Histogram , a core component of our artificial intelligence engine , used for classifying 3D point clouds with a Support Vector Machine . We use Complex Shape Histograms also for loop closing detection in the simultaneous localization and mapping algorithm . We address the problem of classifying 3D point clouds : given 3D urban street scenes gathered by a lidar sensor , we wish to assign a class label to every point . This work is a key step toward realizing applications in robots and cars , for example . This paper addresses classification of 3D point cloud data from natural environments based on voxels . The proposed model uses multi-layer perceptrons to classify voxels based on a statistic geometric analysis of the spatial distribution of inner points . While the most practical cases of such search problems in the context of reinforcement learning are restricted solely to the games context , one motivation of this thesis is to show , that reinforcement learning can also be used to solve such search problems at the frontier of computer vision research . This will be done with special focus on an important part of computer vision that currently faces unsolved challenges , for example because of the large amount of data that accumulate in practical applications : the classification of 3-D point clouds . A wide range of applications such as scene understanding , navigation , or applications in robotics like grasping or scene manipulation would benefit from a reliable and efficient classification of 3-D point clouds . Most of the current 3-D classification systems use so called 3-D feature descriptors to compute lower dimensional descriptions of the entire 3-D point cloud or of local parts of it . With the widely applying of 3D sensors such as LiDAR and RGBD camera in robots and driverless technology , 3D point clouds classification has also achieved some development . PointNet[1 ] is a competitive method in point clouds classification due to it 's fast speed and well performance in real applications . The automatic classification of 3-D point clouds is publicly known as a challenging task in a complex road environment . Specifically , each point is automatically classified into a unique category label , and then , the labels are used as clues for semantic analysis and scene recognition . Thus , Graph-CNNs have huge potential to deal with 3D point cloud data which has been obtained from sampling a manifold . In this paper we develop a Graph-CNN for classifying 3D point cloud data , called PointGCN1 . The architecture combines localized graph convolutions with two types of graph downsampling operations ( also known as pooling ) . It is proposed that aspects of FPS games most salient to gamers were those perceived to be most behaviorally relevant to goal attainment , and that the evaluation of various situational stimuli depended on the extent to which they were perceived either to support or to hinder goal attainment . Implications for the design of FPS games that players experience as challenging , interesting , and fun are discussed . We studied issues in FPS game development such as expressive play , game and character states management , rending scene , maps in game world , online and team communication , weapon management in game , and multi-enemy rendering . In order to increase reusability , maintainability , and quality of game development to adapt to evolutionary game development process , we explored on how to apply design patterns to handle issues with FPS game development . Although some design patterns are discussed , other design patterns might suitable as well because game programming are so complicated to separate each pattern independently . In this paper , we present a technique for constructing a Hierarchical Hidden Markov Model that provides a packet level statistical model for First Person Shooter ( FPS ) gaming network traffic , which allows generation of traffic for various numbers of users through different game states . The proposed solution has been implemented for Counter-strike and Quake as two of the most popular online FPS games . The results derived from the models have then been used to successfully predict certain game related statistics . We observe that critical sections exhibit a pattern of occurrence and can account for upto 17 % of game-play time . We next quantify the expected network induced degradations on critical sections for online FPS games on the Internet . Using traces from a deployment of FPS workloads on 50 + nodes in the Internet , we study network dynamics and their ensuing effect on critical sections . This can allow a variety of network architectures to better deliver higher perceptual experience when deployed on the Internet . Overall , our results have implications for FPS game-design , network provisioning , and game quality evaluation . The competition is ranked based on the number of frags each agent achieves . In this paper , training a competitive agent for playing Doom ’s ( FPS Game ) basic scenario(s ) in a semi-realistic 3D world ‘ VizDoom ’ using the combination of convolutional Deep learning and Q-learning by considering only the screen raw pixels in order to exhibit agent ’s usefulness in Doom is proposed . Experimental results show that the trained agent outperforms average human player and inbuilt game agents in basic scenario(s ) where only move left , right and shoot actions are allowed . We address each of these matters . In particular , a general model for FPS game traffic during the game-play phase is presented . The development of this traffic model was based on measurements of traffic produced by seven popular FPS games . This paper describes Conscientious Objector , a research mod built using the idTech4 engine and assets from the commercial game Doom 3 . It was designed to explore the potential for non-lethal force in FPS gaming , and player response to a radically different avatar-NPC relationship . In multiplayer games , novices can feel overwhelmed if competing against better players , and can fail to improve , which may lead to unsatisfying play and missed social play opportunities . To help novices learn the requisite skills , we first determined how experts accomplish an important task in multiplayer FPS games -- locating their opponent . After determining that an understanding of audio cues and how to leverage them was critical , we designed and evaluated two systems for introducing this skill of locating opponents through audio cues -- a training system , and a modified game interface . Based on the geometrical projection of 3D scattering centers on the line of radar sight , a new method of scattering centers association and 3D imaging of radar targets is proposed . The random sample consensus(RANSAC ) method is utilized to examine the validity of the associated samples and select the optimal associated scheme of each scattering center . This paper presents a new approach for 3D gait estimation from monocular image sequences , using both a kinematics and a walking motion models as sources of prior knowledge . The proposed technique consists of two major stages . We have witnessed the exponential growth of 3D GIS applications which vary from 3D urban management , a heritage city , archaeology , flood controlling , planning and designing , and government planning , to name a few . Thanks to the diversity of 3D models that are used to represent objects in reality and spatial database systems that are utilized to store spatial data , designers and developers can explore and exploit their ability and imagine ultimately to create fantastic applications . AbstractThis paper deals with design methods for medium-complexity mechanical systems , and focuses on two standard steps in such a method : “ 2D drawing ” ( included in the early stages of design ) and “ 3D CADmodel simplification & defeaturing ” ( in the concluding “ analysis stages ” of a design procedure ) , whose contribution to mechanical design has always been a controversial subject . On the basis of the state-of-the-art in mechanical design , this “ position paper ” advocates complete elimination of the above two steps , and details the corresponding modified mechanical-design method . We present a new code aiming at giving a global and coherent approach for transport and turbulence issues in the edge plasma of Tokamaks . The TOKAM-3D code solves 3D fluid drift equations in full-torus geometry including both closed field lines and SOL physics . No scale separation is assumed so that interactions between large scale flows and turbulence are coherently treated . With conclusion of shape and distribution characteristics of TS'UN texture strokes in Chinese Landscape Painting , a sketch-based fractal construction algorithm was proposed to build 3D rock model , and then the baseline of TS'UN texture strokes was generated based on the principle curvature direction of model . The brush model and stylized brush-style model were added to complete the rendering of 3D TS'UN.The results indicate that the algorithm realizes the 3D rendering of TS'UN effect in Chinese Landscape Painting successfully . Malgre les progres realises en vision par ordinateur et en photogrammetrie aerienne , la reconstitution automatique de scene tridimensionnelle a partir des images ou bien des donnees LIDAR restent l’un des problemes complexes et aussi un large champ de recherche . Les modeles 3D precis et detailles des bâtiments ont un interet majeur dans plusieurs domaines tels que l’urbanisme , la navigation , la planification des reseaux de telecommunication et la simulation militaire , ces modeles doivent etres periodiquement alimentes et mis a jour . The online NC Programming and 3D Simulation System for turning worm is developed with VC++ and OpenGL under Windows platform . The system features the functions , such as picture displaying , 3D simulating machining and NC code creation automatically , as well as it is proved to be effective . In this paper , we present a tightly-coupled monocular visual-inertial navigation system ( VINS ) using points and lines with degenerate motion analysis for 3D line triangulation . Based on line segment measurements from images , we propose two sliding window based 3D line triangulation algorithms and compare their performance . This prevents high quality photonic components . High precision 3D printing is utilizing a multiphoton process which is a powerful tool for prototyping of miniaturized designs in automated , scalable processes for products in photonic or medical packaging . While most of the 3D systems are still working rather on a lab than on an industrial scale with typically very long fabrication times ranging from minutes to hours for a single microlens , the process can be boosted significantly to a fabrication time in the range of seconds per lens using different fabrication strategies , resulting in microlenses with high optical quality . The dataset is thus advantageous over the current datasets in terms of the attributes validity . Other car - related research includes detection , tracking , joint detection and pose estimation , and 3D parsing . Fine - grained car models are not explored in these studies . The second stage consists of an embedding step that embeds the high - dimensional feature maps generated by the first stage into low - dimensional , task - specific features . Then , the network performs three different tasks that lead to the 6D pose estimation , i.e. , semantic labeling , 3D translation estimation , and 3D rotation regression , as described next . subsection : Semantic Labeling 3D object coordinate regression network . Since the state - of - the - art 6D pose estimation methods mostly rely on regressing image pixels to 3D object coordinates , we implement a variation of our network for 3D object coordinate regression for comparison . In this network , instead of regressing to center direction and depth as in Fig . section : Introduction Matching 3D geometry has a long history starting in the early days of computer graphics and vision . With the rise of commodity range sensing technologies , this research has become paramount to many applications including object pose estimation , object retrieval , 3D reconstruction , and camera localization . Lin et al . performed a multi - stage sequential refinement using LSTMs to predict 3D pose sequences using previously predicted 2D pose representations and 3D pose . We focus on predicting temporally consistent 3D poses by learning the temporal context of a sequence using a form of sequence - to - sequence network . Unlike Lin et al . [ reference ] , our system for 3D object detection consists of three modules : frustum proposal , 3D instance segmentation , and 3D amodal bounding box estimation . We will introduce each module in the following subsections .
We present here a framework for coupled action recognition and pose estimation by formulating pose estimation as an optimization over a set of action-specific manifolds . The framework allows for integration of a 2D appearance-based action recognition system as a prior for 3D pose estimation and for refinement of the action labels using relational pose features based on the extracted 3D poses . Our experiments show that our pose estimation system is able to estimate body poses with high degrees of freedom using very few particles and can achieve state-of-the-art results on the HumanEva-II benchmark .
PLSA has been successfully used to model the co-occurrence of dyadic data on problems such as image annotation where image features are mapped to word categories via latent variable semantics . We apply the PLSA approach to motion tracking by extending it to a sequential setting where the latent variables describe intrinsic motion semantics linking human figure appearance to 3D pose estimates . This approach is in contrast to many current methods that directly learn the often high-dimensional image-to-pose mappings and utilize subspace projections as a constraint on the pose space alone .
Experiments demonstrate that the proposed algorithm tracks 3D body pose efficiently and accurately , even when self-occlusion , motion blur and large limb movements occur . Quantitative comparisons show that the formulation produces more accurate 3D pose estimates over time than those that can be obtained via a number of previously-proposed particle filtering based tracking algorithms .
The proposed approach employs point correspondences and assumes that the human eye has a spherical shape . The image registration problem is formulated as a 3D pose estimation problem , solved by estimating the rigid transformation that relates the views from which the two images were acquired . Given this estimate , each image can be warped upon the other so that pixels with the same coordinates image the same retinal point .
Our DDL approach aims at simultaneously constructing two overcomplete dictionaries , called the visual observation dictionary ( VBD ) and the body configuration dictionary ( BCD ) , with a shared sparse representation ( SSR ) regularization with respect to every data sample . Under such regularization , two dictionaries are tied together and the 3D pose estimation problem can be reduced to a simple ℓ1 optimization problem given a new test visual observation . We also propose a efficient algorithm based on inexact Augmented Lagrange Multiplier ( IALM ) method to solve the above DDL optimization model .
We demonstrate that pose estimation from the corresponding feature points can be formed as a solution to Sylvester 's equation . We show that the proposed approach to the solution of Sylvester 's equation is equivalent to the classical SVD method for 3D-3D pose estimation . However , whereas classical SVD can not be used for pose estimation directly from 2-D image sequences , our method based on Sylvester 's equation provides a new approach to pose estimation .
We demonstrate that the classical Singular Value Decomposition ( SVD ) approach to pose estimation provides a solution to Sylvester 's equation in 3D-3D pose estimation . The proposed approach to the solution of Sylvester 's equation is therefore equivalent to the classical SVD method for 3D-3D pose estimation , yet it can also be used for pose estimation from 2D image sequences . Finally , we rely on computer simulation experiments to demonstrate the performance of our algorithm on video sequences .
It has the advantage of evaluating related algorithms using non-dimensional metrics for fixed and continuous intervals . In addition , the proposed methodology is validated using a proof of concept scenario based on the 3D pose estimation phase in the 3D reconstruction pipeline . The obtained results are very promising and emphasize the methodology 's generic nature , clearing the way for its application in a multitude of scenarios , such as computer vision and 3D reconstruction .
In this position paper we propose the use of the Distributional Clauses Particle Filter in conjunction with a model-based 3D object tracking method in monocular camera sequences . We describe the model based object tracking method that is based on contour and edge features for 3D pose relative estimation . We also describe the application of the Distributional Clauses Particle Filter that takes into account inputs from object tracking .
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 .
Finding the correct 3D pose that matches the image requires injecting additional information usually in the form of 3D geometric pose priors and temporal or structural constraints . We propose a new joint approach to 2D landmark detection and full 3D pose estimation from a single RGB image that takes advantage of reasoning jointly about the estimation of 2D and 3D landmark locations to improve both tasks . We propose a novel CNN architecture that learns to combine the image appearance based predictions provided by convolutional - pose - machine style 2D landmark detectors , with the geometric 3D skeletal information encoded in a novel pretrained model of 3D human pose .
Another recent example is Bogo , who fit a detailed statistical 3D body model to 2D joint proposals . Zhou tackles the problem of 3D pose estimation for a monocular image sequence integrating 2D , 3D and temporal information to account for uncertainties in the model and the measurements . Similar to our proposed approach , Zhou ’s method does not need synchronized 2D - 3D training data , it only needs 2D pose annotations to train the CNN joint regressor and a separate 3D mocap dataset to learn the 3D sparse basis .
• We firstly cast the problem of estimating 3D pose from a single depth map into a voxel - to - voxel prediction . Unlike most of the previous methods that regress 3D coordinates directly from the 2D depth image , our proposed V2V - PoseNet estimates the per - voxel likelihood from a voxelized grid input .
It also allows us to win the 3D hand pose estimation challenge . As voxel - tovoxel prediction is firstly tried in 3D hand and human pose estimation from a single depth map , we hope this work to provide a new way of accurate 3D pose estimation . section :
We design a software which applies our algorithm to various datasets . Experiments on 3D point clouds , mineral structures , and images show the effectiveness of our algorithm in practice .
The second mobility aspect is related with a novel Software as a Service system that allows access to robotic functionalities and data over the Ethernet , thus we demonstrate the use of the novel NVIDIA GRID technology allowing to virtualize the graphic processing unit . We introduce Complex Shape Histogram , a core component of our artificial intelligence engine , used for classifying 3D point clouds with a Support Vector Machine . We use Complex Shape Histograms also for loop closing detection in the simultaneous localization and mapping algorithm .
We address the problem of classifying 3D point clouds : given 3D urban street scenes gathered by a lidar sensor , we wish to assign a class label to every point . This work is a key step toward realizing applications in robots and cars , for example .
This paper addresses classification of 3D point cloud data from natural environments based on voxels . The proposed model uses multi-layer perceptrons to classify voxels based on a statistic geometric analysis of the spatial distribution of inner points .
While the most practical cases of such search problems in the context of reinforcement learning are restricted solely to the games context , one motivation of this thesis is to show , that reinforcement learning can also be used to solve such search problems at the frontier of computer vision research . This will be done with special focus on an important part of computer vision that currently faces unsolved challenges , for example because of the large amount of data that accumulate in practical applications : the classification of 3-D point clouds . A wide range of applications such as scene understanding , navigation , or applications in robotics like grasping or scene manipulation would benefit from a reliable and efficient classification of 3-D point clouds . Most of the current 3-D classification systems use so called 3-D feature descriptors to compute lower dimensional descriptions of the entire 3-D point cloud or of local parts of it .
With the widely applying of 3D sensors such as LiDAR and RGBD camera in robots and driverless technology , 3D point clouds classification has also achieved some development . PointNet[1 ] is a competitive method in point clouds classification due to it 's fast speed and well performance in real applications .
The automatic classification of 3-D point clouds is publicly known as a challenging task in a complex road environment . Specifically , each point is automatically classified into a unique category label , and then , the labels are used as clues for semantic analysis and scene recognition .
Thus , Graph-CNNs have huge potential to deal with 3D point cloud data which has been obtained from sampling a manifold . In this paper we develop a Graph-CNN for classifying 3D point cloud data , called PointGCN1 . The architecture combines localized graph convolutions with two types of graph downsampling operations ( also known as pooling ) .
It is proposed that aspects of FPS games most salient to gamers were those perceived to be most behaviorally relevant to goal attainment , and that the evaluation of various situational stimuli depended on the extent to which they were perceived either to support or to hinder goal attainment . Implications for the design of FPS games that players experience as challenging , interesting , and fun are discussed .
We studied issues in FPS game development such as expressive play , game and character states management , rending scene , maps in game world , online and team communication , weapon management in game , and multi-enemy rendering . In order to increase reusability , maintainability , and quality of game development to adapt to evolutionary game development process , we explored on how to apply design patterns to handle issues with FPS game development . Although some design patterns are discussed , other design patterns might suitable as well because game programming are so complicated to separate each pattern independently .
In this paper , we present a technique for constructing a Hierarchical Hidden Markov Model that provides a packet level statistical model for First Person Shooter ( FPS ) gaming network traffic , which allows generation of traffic for various numbers of users through different game states . The proposed solution has been implemented for Counter-strike and Quake as two of the most popular online FPS games . The results derived from the models have then been used to successfully predict certain game related statistics .
We observe that critical sections exhibit a pattern of occurrence and can account for upto 17 % of game-play time . We next quantify the expected network induced degradations on critical sections for online FPS games on the Internet . Using traces from a deployment of FPS workloads on 50 + nodes in the Internet , we study network dynamics and their ensuing effect on critical sections .
This can allow a variety of network architectures to better deliver higher perceptual experience when deployed on the Internet . Overall , our results have implications for FPS game-design , network provisioning , and game quality evaluation .
The competition is ranked based on the number of frags each agent achieves . In this paper , training a competitive agent for playing Doom ’s ( FPS Game ) basic scenario(s ) in a semi-realistic 3D world ‘ VizDoom ’ using the combination of convolutional Deep learning and Q-learning by considering only the screen raw pixels in order to exhibit agent ’s usefulness in Doom is proposed . Experimental results show that the trained agent outperforms average human player and inbuilt game agents in basic scenario(s ) where only move left , right and shoot actions are allowed .
We address each of these matters . In particular , a general model for FPS game traffic during the game-play phase is presented . The development of this traffic model was based on measurements of traffic produced by seven popular FPS games .
This paper describes Conscientious Objector , a research mod built using the idTech4 engine and assets from the commercial game Doom 3 . It was designed to explore the potential for non-lethal force in FPS gaming , and player response to a radically different avatar-NPC relationship .
In multiplayer games , novices can feel overwhelmed if competing against better players , and can fail to improve , which may lead to unsatisfying play and missed social play opportunities . To help novices learn the requisite skills , we first determined how experts accomplish an important task in multiplayer FPS games -- locating their opponent . After determining that an understanding of audio cues and how to leverage them was critical , we designed and evaluated two systems for introducing this skill of locating opponents through audio cues -- a training system , and a modified game interface .
Based on the geometrical projection of 3D scattering centers on the line of radar sight , a new method of scattering centers association and 3D imaging of radar targets is proposed . The random sample consensus(RANSAC ) method is utilized to examine the validity of the associated samples and select the optimal associated scheme of each scattering center .
This paper presents a new approach for 3D gait estimation from monocular image sequences , using both a kinematics and a walking motion models as sources of prior knowledge . The proposed technique consists of two major stages .
We have witnessed the exponential growth of 3D GIS applications which vary from 3D urban management , a heritage city , archaeology , flood controlling , planning and designing , and government planning , to name a few . Thanks to the diversity of 3D models that are used to represent objects in reality and spatial database systems that are utilized to store spatial data , designers and developers can explore and exploit their ability and imagine ultimately to create fantastic applications .
AbstractThis paper deals with design methods for medium-complexity mechanical systems , and focuses on two standard steps in such a method : “ 2D drawing ” ( included in the early stages of design ) and “ 3D CADmodel simplification & defeaturing ” ( in the concluding “ analysis stages ” of a design procedure ) , whose contribution to mechanical design has always been a controversial subject . On the basis of the state-of-the-art in mechanical design , this “ position paper ” advocates complete elimination of the above two steps , and details the corresponding modified mechanical-design method .
We present a new code aiming at giving a global and coherent approach for transport and turbulence issues in the edge plasma of Tokamaks . The TOKAM-3D code solves 3D fluid drift equations in full-torus geometry including both closed field lines and SOL physics . No scale separation is assumed so that interactions between large scale flows and turbulence are coherently treated .
With conclusion of shape and distribution characteristics of TS'UN texture strokes in Chinese Landscape Painting , a sketch-based fractal construction algorithm was proposed to build 3D rock model , and then the baseline of TS'UN texture strokes was generated based on the principle curvature direction of model . The brush model and stylized brush-style model were added to complete the rendering of 3D TS'UN.The results indicate that the algorithm realizes the 3D rendering of TS'UN effect in Chinese Landscape Painting successfully .
Malgre les progres realises en vision par ordinateur et en photogrammetrie aerienne , la reconstitution automatique de scene tridimensionnelle a partir des images ou bien des donnees LIDAR restent l’un des problemes complexes et aussi un large champ de recherche . Les modeles 3D precis et detailles des bâtiments ont un interet majeur dans plusieurs domaines tels que l’urbanisme , la navigation , la planification des reseaux de telecommunication et la simulation militaire , ces modeles doivent etres periodiquement alimentes et mis a jour .
The online NC Programming and 3D Simulation System for turning worm is developed with VC++ and OpenGL under Windows platform . The system features the functions , such as picture displaying , 3D simulating machining and NC code creation automatically , as well as it is proved to be effective .
In this paper , we present a tightly-coupled monocular visual-inertial navigation system ( VINS ) using points and lines with degenerate motion analysis for 3D line triangulation . Based on line segment measurements from images , we propose two sliding window based 3D line triangulation algorithms and compare their performance .
This prevents high quality photonic components . High precision 3D printing is utilizing a multiphoton process which is a powerful tool for prototyping of miniaturized designs in automated , scalable processes for products in photonic or medical packaging . While most of the 3D systems are still working rather on a lab than on an industrial scale with typically very long fabrication times ranging from minutes to hours for a single microlens , the process can be boosted significantly to a fabrication time in the range of seconds per lens using different fabrication strategies , resulting in microlenses with high optical quality .
The dataset is thus advantageous over the current datasets in terms of the attributes validity . Other car - related research includes detection , tracking , joint detection and pose estimation , and 3D parsing . Fine - grained car models are not explored in these studies .
The second stage consists of an embedding step that embeds the high - dimensional feature maps generated by the first stage into low - dimensional , task - specific features . Then , the network performs three different tasks that lead to the 6D pose estimation , i.e. , semantic labeling , 3D translation estimation , and 3D rotation regression , as described next . subsection : Semantic Labeling
3D object coordinate regression network . Since the state - of - the - art 6D pose estimation methods mostly rely on regressing image pixels to 3D object coordinates , we implement a variation of our network for 3D object coordinate regression for comparison . In this network , instead of regressing to center direction and depth as in Fig .
section : Introduction Matching 3D geometry has a long history starting in the early days of computer graphics and vision . With the rise of commodity range sensing technologies , this research has become paramount to many applications including object pose estimation , object retrieval , 3D reconstruction , and camera localization .
Lin et al . performed a multi - stage sequential refinement using LSTMs to predict 3D pose sequences using previously predicted 2D pose representations and 3D pose . We focus on predicting temporally consistent 3D poses by learning the temporal context of a sequence using a form of sequence - to - sequence network . Unlike Lin et al .
[ reference ] , our system for 3D object detection consists of three modules : frustum proposal , 3D instance segmentation , and 3D amodal bounding box estimation . We will introduce each module in the following subsections .
Coupled Action Recognition and Pose Estimation from Multiple Views Monocular 3D Human Motion Tracking Using Dynamic Probabilistic Latent Semantic Analysis Monocular Tracking of 3D Human Motion with a Coordinated Mixture of Factor Analyzers Retinal image registration under the assumption of a spherical eye Robust 3D human pose estimation via dual dictionaries learning A Particle Filtering Framework for Joint Video Tracking and Pose Estimation Pose estimation from video sequences based on Sylvester's equation Towards a Standalone Methodology for Robust Algorithms Evaluation: A Case Study in 3D Reconstruction Application of Dynamic Distributional Clauses for multi-hypothesis initialization in model-based object tracking Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map Persistent 1-Cycles: Definition, Computation, and Its Application Intelligent Mobile System for Improving Spatial Design Support and Security Inside Buildings Super-Segments Based Classification of 3D Urban Street Scenes Multi-layer Perceptrons for Voxel-Based Classification of Point Clouds from Natural Environments Learning strategies to select point cloud descriptors for large-scale 3-D object classification Weight Loss for Point Clouds Classification A Novel Octree-Based 3-D Fully Convolutional Neural Network for Point Cloud Classification in Road Environment A Graph-CNN for 3D Point Cloud Classification How computer gamers experience the game situation: a behavioral study Design patterns applied for networked first person shooting game programming A Statistical Network Traffic Model for First-Person Shooter Games Critical sections in networked games Training an Agent for FPS Doom Game using Visual Reinforcement Learning and VizDoom Traffic modelling of on-line first person shooter games Conscientious Objector: Pacifism, Politics and Abusing the Player in Doom 3 Scaffolding Player Location Awareness through Audio Cues in First-Person Shooters New method of 3D imaging based on scattering centers association 3D gait estimation from monoscopic video A comparative survey of 3D GIS models 3D methodology for modeling and analysis of medium-complexity mechanical assemblies: Application in elevator-car design TOKAM-3D: A 3D fluid code for transport and turbulence in the edge plasma of Tokamaks Graphical Simulation Algorithm of 3D TS'UN for Chinese Landscape Painting Modélisation automatique des données LIDAR Research on NC Programming and 3D Simulation System of Turning Worm Visual-Inertial Odometry with Point and Line Features From Lab to Fab — High‐Precision 3D Printing A large-scale car dataset for fine-grained categorization and verification PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions Exploiting Temporal Information for 3D Human Pose Estimation Frustum PointNets for 3D Object Detection from RGB-D Data
5bee31cc0c2e5a3149b366b36bd3f7fbcc45f7a8 6e41f695cdc07e1f6e6367ec8b05baaad101e567 ae3c6c70f2bdb24f86edadb80bec32331080268c 72ad24f351d3dcd23ed56179a89212375b62435e 0f95cb91674a0f387087c3e3cc52d772215ba380 6f923768a18ee12ad83ce91e7d239f22b5ed4edc e6e42b94e655b3ae3bc8056c08f7f35bec56ae69 4478ba6165703b8d6889e40085764251f2f9beff 2082b4e30226563bd0c54e9171ffec68d2bf414a b3237f0be07642cbf3f6d6ed453c1b155681158d e07f6efc21b623348ed426d7f9847c2a9a391435 60e3295ff63a458528a33bd4f3025fca6f9d2577 234aa9d9ce5782f8de005c1cbac216f1b90f752e 90348772b3c699b4998d13abd3dcb2724c7b6fa3 be7fdfb185522dd26b75c5c4a9c9f6e2d6f1e26d 2cc6fe47f3e76dd880d872e88262acbab83094df 1a88440f6a345969b14275b2844e2c1b7406b47e cbe595a4e08ad73daed983356edf2fd0c09f7de2 a88082d46955fb924f3bd1a285773c81bd8111f0 db3b3274d687dd39212ea76e961ea19b13f4940a d38dfdd1cb59bebafc8d729a59f36be0d6060054 74c35bb13e71cdd8b5a553a7e65d9ed125ce958e 174e5c822cb2370cb0b17c045e1553666a4edc86 e39c612e03e5de2ad2024b1ce5c70464e19ef704 bd4444c2ced4e02701e81ea912b0768627225605 b3b0600e73fb3e703c37e277740c6bddb0257506 07ec7022e04e9986f844fafc97ea23fcbf9441df 4c409afed66813481f003b62faea1e137999eb98 5fe2d53c0b1b1628899c0ab60aecf5a52d5e3e57 50bdb7ea1449569ea179c412cc1d5541302b4c96 fb1b48b977807bd58270e3de1956ae6b0bef43db 5ed1eb8efcad0087371ea9ebc62510d9cb484250 3b45e0b9270a4cc6d42e2376ef901be92875ea74 bdfa21fa1a12d144303609edcf5790bb3b86d228 2f454e73a871c4c4ae91ed7ed4d478889dcbc06e
["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics","Medicine"] ["Computer Science"] ["Mathematics","Medicine","Computer Science"] ["Computer Science","Engineering","Mathematics"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Political Science","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics"] ["Physics","Computer Science"] ["Computer Science"] ["Biology"] ["Computer Science"] ["Computer Science"] ["Physics"]
2012 2008 2006 2017 2012 2010 2007 2011 2014 2017 2018 2019 2016 2012 2015 2016 2019 2019 2018 2006 2014 2014 2013 2017 2014 2008 2016 2011 2004 2017 2012 2010 2009 2015 2005 2019 2017 2015 2018 2017 2018 2018
ICPR VCIP SIBGRAPI VISAPP (2) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition CTIC IWANN (2) ICASSP SNPD ICC ICEC CHI ICIP IROS 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Robotics: Science and Systems 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ECCV 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
https://api.semanticscholar.org/5bee31cc0c2e5a3149b366b36bd3f7fbcc45f7a8 https://api.semanticscholar.org/6e41f695cdc07e1f6e6367ec8b05baaad101e567 https://api.semanticscholar.org/ae3c6c70f2bdb24f86edadb80bec32331080268c https://api.semanticscholar.org/72ad24f351d3dcd23ed56179a89212375b62435e https://api.semanticscholar.org/0f95cb91674a0f387087c3e3cc52d772215ba380 https://api.semanticscholar.org/6f923768a18ee12ad83ce91e7d239f22b5ed4edc https://api.semanticscholar.org/e6e42b94e655b3ae3bc8056c08f7f35bec56ae69 https://api.semanticscholar.org/4478ba6165703b8d6889e40085764251f2f9beff https://api.semanticscholar.org/2082b4e30226563bd0c54e9171ffec68d2bf414a https://www.semanticscholar.org/paper/4be92654f72410d20d3a6a57a5c434b619d8605e https://www.semanticscholar.org/paper/119ae73801f8bbecd8541f7794d2ee05ebcac108 https://api.semanticscholar.org/b3237f0be07642cbf3f6d6ed453c1b155681158d https://api.semanticscholar.org/e07f6efc21b623348ed426d7f9847c2a9a391435 https://api.semanticscholar.org/60e3295ff63a458528a33bd4f3025fca6f9d2577 https://api.semanticscholar.org/234aa9d9ce5782f8de005c1cbac216f1b90f752e https://api.semanticscholar.org/90348772b3c699b4998d13abd3dcb2724c7b6fa3 https://api.semanticscholar.org/be7fdfb185522dd26b75c5c4a9c9f6e2d6f1e26d https://api.semanticscholar.org/2cc6fe47f3e76dd880d872e88262acbab83094df https://api.semanticscholar.org/1a88440f6a345969b14275b2844e2c1b7406b47e https://api.semanticscholar.org/cbe595a4e08ad73daed983356edf2fd0c09f7de2 https://api.semanticscholar.org/a88082d46955fb924f3bd1a285773c81bd8111f0 https://api.semanticscholar.org/db3b3274d687dd39212ea76e961ea19b13f4940a https://api.semanticscholar.org/d38dfdd1cb59bebafc8d729a59f36be0d6060054 https://api.semanticscholar.org/74c35bb13e71cdd8b5a553a7e65d9ed125ce958e https://api.semanticscholar.org/174e5c822cb2370cb0b17c045e1553666a4edc86 https://api.semanticscholar.org/e39c612e03e5de2ad2024b1ce5c70464e19ef704 https://api.semanticscholar.org/bd4444c2ced4e02701e81ea912b0768627225605 https://api.semanticscholar.org/b3b0600e73fb3e703c37e277740c6bddb0257506 https://api.semanticscholar.org/07ec7022e04e9986f844fafc97ea23fcbf9441df https://api.semanticscholar.org/4c409afed66813481f003b62faea1e137999eb98 https://api.semanticscholar.org/5fe2d53c0b1b1628899c0ab60aecf5a52d5e3e57 https://api.semanticscholar.org/50bdb7ea1449569ea179c412cc1d5541302b4c96 https://api.semanticscholar.org/fb1b48b977807bd58270e3de1956ae6b0bef43db https://api.semanticscholar.org/5ed1eb8efcad0087371ea9ebc62510d9cb484250 https://api.semanticscholar.org/3b45e0b9270a4cc6d42e2376ef901be92875ea74 https://api.semanticscholar.org/bdfa21fa1a12d144303609edcf5790bb3b86d228 https://api.semanticscholar.org/2f454e73a871c4c4ae91ed7ed4d478889dcbc06e https://www.semanticscholar.org/paper/496c6db97a4f16e6bc9e4d63af420eb66f900f74 https://www.semanticscholar.org/paper/1c7e078611c9df412e6eb3a356f31a0da0c1f99c https://www.semanticscholar.org/paper/5b589d7564dbef07ec98b3248f58a481b4ca1395 https://www.semanticscholar.org/paper/38cc89399dd6f5aaab1654f27ab3c9eeade12a36 https://www.semanticscholar.org/paper/526cf249c2760b7bdbb28f2a2a7c85851d3c2727