Recent advances in 3D deep learning have shown that it is possible to train highly effective deep models for 3D shape generation , directly from 2D images . This is particularly interesting since the availability of 3D models is still limited compared to the massive amount of accessible 2D images , which is invaluable for training . Our method achieves the state-of-the-art performance for 3D shape retrieval , on two large-scale 3D shape datasets , ModelNet and ShapeNetCore55 . Extensive experiments show that the proposed 3D representation performs robust discrimination against view occlusion , and strong generation ability for various 3D shape tasks . The proposed 3D representation has following advantages against other state-of-the-art : 1 ) it performs robust discrimination under the existence of noise such as view missing and occlusion , because of the improvement brought by 3D properties . 2 ) it has strong generative capabilities , which is useful for various 3D shape tasks . We evaluate ERFA-Net on two popular 3D shape datasets , ModelNet and ShapeNetCore55 , and ERFA-Net outperforms the state-of-the-art methods significantly . The problem is challenging due to highly non-linear deformations , high dimensionality , and data sparsity . Several new algorithms are proposed for 3D shape modelling , 3D shape matching ( computing dense correspondence ) and applications . First , we propose a variant of Principal Component Analysis called “ Shell PCA ” which provides a physically-inspired statistical shape model . It is shown that the resultant flow is composed of the well known mean curvature and flux maximizing flows . In particular , we apply the proposed algorithm to the problem of 3D shape modelling in a multiview photometric stereo setting . Experimental results for various synthetic data show the validity of our approach . Learning powerful deep generative models for 3D shape synthesis is largely hindered by the difficulty in ensuring plausibility encompassing correct topology and reasonable geometry . Indeed , learning the distribution of plausible 3D shapes seems a daunting task for the holistic approaches , given the significant topological variations of 3D objects even within the same category . The Adaptive O-CNN encoder takes the planar patch normal and displacement as input and performs 3D convolutions only at the octants at each level , while the Adaptive O-CNN decoder infers the shape occupancy and subdivision status of octants at each level and estimates the best plane normal and displacement for each leaf octant . As a general framework for 3D shape analysis and generation , the Adaptive O-CNN not only reduces the memory and computational cost , but also offers better shape generation capability than the existing 3D-CNN approaches . We validate Adaptive O-CNN in terms of efficiency and effectiveness on different shape analysis and generation tasks , including shape classification , 3D autoencoding , shape prediction from a single image , and shape completion for noisy and incomplete point clouds . 〈 Summary 〉 In recent years , many line drawing algorithms have been proposed for 3D shape depiction . One common drawback is that these algorithms draw only part of the necessary lines for fully depicting the shape . Adding interactive haptic-constraint sensations is important in interactive computer gaming and 3D shape design . Usually constraints are set on vertices of the object to drive the deformation . A new class of dynamic volume completion is introduced , where image elements ( e.g. , occluding semi-ellipses placed at the edge of an object ) can link across a gap between two or more objects , leading to the perception of illusory volumes that deform as those image elements are set into relative motion . These new demonstrations provide further evidence that volume completion is not dictated solely by contour relatability constraints , but is instead a dynamic process of 3D shape construction that also takes into account dynamic cues to object shape , even in the absence of any contour relatability whatsoever . A classification of VR-based methods is proposed : real-time rotoscopy methods , real-time direct metaphors and real-time recognition-based metaphors . Several examples are presented : 3D shape creation , camera motion , body motion control , hand animation , and facial animation . The hardware and software architecture of our animation system is also described . We present multiresolution tree-structured networks to process point clouds for 3D shape understanding and generation tasks . Our network represents a 3D shape as a set of locality-preserving 1D ordered list of points at multiple resolutions . Thorough reviews of the literature reveal a significant amount of publications concerning tactile human- machine interfaces , especially onwards the ~1990 . This paper reports the progress in tactile interfaces technology in the following areas : teleoperation , telepresence , sensory substitution , 3D surface generation , Braille systems , laboratory prototypes , and games . Different parameters and specifications required to generate and feed back tactile sensation are summarized . In this paper is introduced an image guided oral implant system ( IGOIS ) , including the 3D surface-model generation through Marching Cubes algorithm and large-scale triangular mesh simplification , the realization of pre-operative planning module with computer graphics and image processing technology , the non-invasive point-to-point registration with the fabrication of tooth-supported polymer resin templates and ICP algorithm , and the development of the real-time navigation system software by programming in VTK and VC+ + 6.0 . The experimental test for a patient 's rapid prototype model shows that with the support of IGOIS , the precision achieved in the planning phase can be transferred to the patient so that the accuracy of the oral implant surgery under difficult conditions is improved . We present a software package which uniquely integrates several facets of image-guided medicine into a single portable , extendable environment . It provides capabilities for automatic registration , semi-automatic segmentation , 3D surface model generation , 3D visualization , and quantitative analysis of various medical scans . We describe its system architecture , wide range of applications , and novel integration with an interventional Magnetic Resonance ( MR ) scanner to augment intra-operative imaging with pre-operative data . The method was tested on two buildings . The program package Photomodeler version 5 was used , enabling the complete project accomplishment that includes camera calibration , image measurements , adjustments of object coordinates , generation of surface 3D models and photorendering . The phases of the project are described as well as limitations and problems encountered in practice . This thesis discusses the application of the track function of geometric drawing board in 3-D surface drawing , and introduces the ways of drawing the surface under the " track function " based on the basic network , and drawing the surface under the " pursue function " by setting up three dimensional coordinate system . We have developed a method to simulate complex thermal scenes under variable solar illumination , different materials and including gas plumes . The IDL-based scene simulation tool consists of public domain tools for : 3D geometry generation , raytracing , spectral and thermal property libraries , atmospheric transmission and emission modeling . The data can then be used in standard hyperspectral processing programs or processed by special purpose programs . We have been developing Spherical Medial Representation ( SM-Rep ) based on skeletonisation over the sphere . While M-Rep provides a paradigm for 3D surface and image analysis , the resulting surface remains in a 3D vector space . Since the geometry of the unit sphere is quite different , especially geodesies , SM-Rep supplies consistent elements to overcome this issue . Also another based on partial resampling is used to paste a 2.5D model to any other 3D model , and has made some good results . The work of this paper has greatly shortened the path from 2D editing to 3D surface animation . In this paper , I propose the image-based 3D modeling system using calibrated camera . The proposed algorithm for rendering 3D model is consisted of three steps , camera calibration , 3D shape reconstruction and 3D surface generation step . In the camera calibration step , I estimate the camera matrix for the image aquisition camera . Extensive pre-processing of the imagery was performed to prepare the data for this purpose . The steps incorporated for this purpose consist of 1 ) Data Calibration , 2 ) Mosaicking , 3 ) Pan Sharpening , 4 ) Generation of 3D surface , and 5 ) Digitization . Using image classification as the primary method of information extraction , this study tests the ever-popular pixel-based classification technique against the uprising object-based classification technique . [ reference ] ) , i.e. a mesh , which is more desirable for many real applications since it is lightweight , capable of modelling shape details , easy to deform for animation , to name a few . In this paper , we push along the direction of single image reconstruction , and propose an algorithm to extract a 3D triangular mesh from a single color image . Rather than directly synthesizing , our model learns to deform a mesh from a mean shape to the target geometry . Neuron models were optimized to reproduce electrophysiological properties of major classes of M1 neurons . Layer 5 corticospinal and corticostriatal neuron morphologies with 700 + compartments reproduced cell 3D reconstructions , and their ionic channel distributions were optimized within experimental constraints to reproduce in vitro recordings . The network was driven by the main long-range inputs to M1 : posterior nucleus ( PO ) and ventrolateral ( VL ) thalamus PO , primary and secondary somatosensory cortices ( S1 , S2 ) , contralateral M1 , secondary motor cortex ( M2 ) , and orbital cortex ( OC ) . The methodology sets a library of modules that can be used to assemble a solid geometric LN model and subsequently generate an adaptive mesh model capable of implementing transport phenomena . Overall , based on the use of high-resolution confocal microscopy and morphological analysis of cell 3D reconstructions , we have developed a computational model of the LN geometry , suitable for further investigation in studies of fluid transport and cell migration in this immunologically essential organ . Patterned illumination is a successful set of techniques in high resolution 3D microscopy . In particular , structured illumination microscopy is based on the projection of 1D periodic patterns onto the 3D sample under study . High quality depth map estimation is required for better visualization of 3D views as there is great impact of depth map quality on overall 3D image quality . If the depth is estimated from conventional ways using two or more images , some defects come into picture , mostly in regions without texture . ” It gave them the idea for 3DNetMod , their new computational method to analyze 3D genome-folding . Topologically associating domains ( TADs ) are chromosomal compartments within which other TADs can be nested . This poster deals with the problem of sensing virtual environments for 3D intelligent multi-character simulations . As these creatures should display reactive skills ( navigation or gazing ) , together with the necessary planning processes , required to animate their behaviours , we present an efficient and fully scalable sensor system designed to provide this information ( low level + high level ) to different kinds of 3D embodied agents ( games , storytelling , etc ) . The lag between head motion and display response results in motion sickness , indisposition , and , at worst , abortion of the telepresence session . In this paper , we propose a delay compensation approach for 3D 360 ° telepresence systems realized with a mechanically actuated stereoscopic vision system . We further introduce a novel metric to evaluate the achievable level of delay compensation . In this paper , a novel approach for creating 3D models of building scenes is presented . The proposed method is fully automated and fast , and accurately reconstructs both outdoor images of a building and indoor scenes , with perspective cues in real-time , using only one image . Electronic product tags are the current standard for authentication purposes ; however , often this technology is neither secure nor cost-effective , and fails to take advantage of other unique 3D printing features . Considering the great usability of the QR code , we are motivated to enhance the QR code for the practical and cost-effective 3D printing product identification . Particularly , we bring up the all-in-one design , all-in-one manufacturing concept incorporating the QR code in the complete 3D printing paradigm . The problem can be thought as a combination of three main approaches . ( 1 ) Image reconstruction of 3D artery volume from few number of projections ( each is presented as 2D image ) , ( 2 ) automatic detection of the catheter roadmap to the labeled artery which feed the tumor , and ( 3 ) interactive system to control and display images using simple gestures of the physician . MDCT scanners have significantly improved the image quality by dramatically reducing artifacts resulting from partial volume averaging and motion . Additionally , fast and user-friendly scanner workstations allow for post-processing of impressive multiplanar 2D and 3D reconstructions , which are desirable to clinicians for comprehensive pre-surgical planning purposes [ 1 , 2 ] . In , the 3DMM parameters are estimated in six steps each of which employs a different CNN . Notably , estimates the 3DMM parameters on a sparse set of landmarks , i.e. the purpose of is 3D face alignment rather than face reconstruction . The method of is currently considered the state - of - the - art in 3DMM fitting . In this work , we introduce PoseCNN , a new Convolutional Neural Network for 6D object pose estimation . PoseCNN estimates the 3D translation of an object by localizing its center in the image and predicting its distance from the camera . The 3D rotation of the object is estimated by regressing to a quaternion representation . section : Conclusion We introduced a viewpoint invariant model that estimates 3D human pose from a single depth image . Our model is formulated as a deep discriminative model that attends to glimpses in the input . [ reference ] [ reference ] . subsection : 3D Image Rotation When the depth information is estimated , the face image can be rotated in 3D space to generate the appearances in larger poses ( Fig . We therefore get the identities o of the visible object ( s ) , and for these objects , we use the segmentation centroids as their 2D centers , to compute the 3D poses of the objects as described below . section : Predicting the 3D Pose We predict the 3D pose of an object by applying a Deep Network to an image window W centered on the 2D object center estimated as described in the previous section . This results in , where is the mean texture vector and is the orthonormal basis after keeping the first principal components . This model can be used to generate novel 3D feature - based texture instances with the function as where are the texture parameters . Finally , an iterative procedure is used in order to refine the texture . The comparative simulation experimental results between our method and the traditional methods , such as least-squares and eigenvalue method , are provided . Moreover , our method is applied to the experiment of 3D laser scanning point cloud planes for the attitude estimation . The method is validated on both simulated data and real data . 3D Plane Detection and Reconstruction From a Single Image . This paper proposes a deep neural architecture , PlaneRCNN , that detects and reconstructs piecewise planar regions from a single RGB image . Such an inter-linked framework provides a holistic approach to scene understanding , while obtaining the best possible detection rates . Motivated by recent research in coherent geometrical contextual reasoning and object recognition , this paper proposes a unified framework for robust 3D supporting plane estimation using a joint probabilistic model which uses results from object shape detection and 3D plane estimation . Maximization of the joint probabilistic model leads to robust 3D surface estimation while reducing false perceptual grouping . The presented approach is suitable for the calibration of rigs composed by N cameras modelled by N different models . Calibration results , compared with state of the art approaches , and a 3D plane estimation application , allowed by the calibration , show the effectiveness of the approach . A cross-platform software implementing this method is available1 . The algorithm 's operation is simple , adaptable and flexible . The algorithm is a new algorithm for 3D plane recognition , and the effectiveness of the algorithm has been verified by experiments . Since the introduction of the Microsoft Kinect sensor , real-time 3D perception of objects in a close-range scene has become one of the major trends in the current research on robotics and computer vision domain . As part of a long-term goal to develop a robust object recognition system for industrial applications , this paper focuses on the research topic of 3D planes recognition and separation for polyhedral object grasping using industrial manipulators and Kinect as a 3D vision system . Since the data acquired by the vision sensor such as Kinect are invariably noisy , a pipeline of algorithms has been implemented to reduce the noise and clustering the data points properly . However , it suffers from the spurious-plane problem when noise and outliers exist due to the uncertainty of randomly sampling the minimum subset with 3 points . An improved RANSAC method based on Normal Distribution Transformation ( NDT ) cells is proposed in this study to avoid spurious planes for 3D point-cloud plane segmentation . A planar NDT cell is selected as a minimal sample in each iteration to ensure the correctness of sampling on the same plane surface . This paper presents a combined bottom-up and top-down approach to 3D roof plane detection and segmentation from laser scanning point clouds . Laser scanning data of city scenes often shows noise and incompleteness because of , e.g. , the clutter by trees and the reflection of windows/waterlogged depressions on the roof .
Recent advances in 3D deep learning have shown that it is possible to train highly effective deep models for 3D shape generation , directly from 2D images . This is particularly interesting since the availability of 3D models is still limited compared to the massive amount of accessible 2D images , which is invaluable for training .
Our method achieves the state-of-the-art performance for 3D shape retrieval , on two large-scale 3D shape datasets , ModelNet and ShapeNetCore55 . Extensive experiments show that the proposed 3D representation performs robust discrimination against view occlusion , and strong generation ability for various 3D shape tasks .
The proposed 3D representation has following advantages against other state-of-the-art : 1 ) it performs robust discrimination under the existence of noise such as view missing and occlusion , because of the improvement brought by 3D properties . 2 ) it has strong generative capabilities , which is useful for various 3D shape tasks . We evaluate ERFA-Net on two popular 3D shape datasets , ModelNet and ShapeNetCore55 , and ERFA-Net outperforms the state-of-the-art methods significantly .
The problem is challenging due to highly non-linear deformations , high dimensionality , and data sparsity . Several new algorithms are proposed for 3D shape modelling , 3D shape matching ( computing dense correspondence ) and applications . First , we propose a variant of Principal Component Analysis called “ Shell PCA ” which provides a physically-inspired statistical shape model .
It is shown that the resultant flow is composed of the well known mean curvature and flux maximizing flows . In particular , we apply the proposed algorithm to the problem of 3D shape modelling in a multiview photometric stereo setting . Experimental results for various synthetic data show the validity of our approach .
Learning powerful deep generative models for 3D shape synthesis is largely hindered by the difficulty in ensuring plausibility encompassing correct topology and reasonable geometry . Indeed , learning the distribution of plausible 3D shapes seems a daunting task for the holistic approaches , given the significant topological variations of 3D objects even within the same category .
The Adaptive O-CNN encoder takes the planar patch normal and displacement as input and performs 3D convolutions only at the octants at each level , while the Adaptive O-CNN decoder infers the shape occupancy and subdivision status of octants at each level and estimates the best plane normal and displacement for each leaf octant . As a general framework for 3D shape analysis and generation , the Adaptive O-CNN not only reduces the memory and computational cost , but also offers better shape generation capability than the existing 3D-CNN approaches . We validate Adaptive O-CNN in terms of efficiency and effectiveness on different shape analysis and generation tasks , including shape classification , 3D autoencoding , shape prediction from a single image , and shape completion for noisy and incomplete point clouds .
〈 Summary 〉 In recent years , many line drawing algorithms have been proposed for 3D shape depiction . One common drawback is that these algorithms draw only part of the necessary lines for fully depicting the shape .
Adding interactive haptic-constraint sensations is important in interactive computer gaming and 3D shape design . Usually constraints are set on vertices of the object to drive the deformation .
A new class of dynamic volume completion is introduced , where image elements ( e.g. , occluding semi-ellipses placed at the edge of an object ) can link across a gap between two or more objects , leading to the perception of illusory volumes that deform as those image elements are set into relative motion . These new demonstrations provide further evidence that volume completion is not dictated solely by contour relatability constraints , but is instead a dynamic process of 3D shape construction that also takes into account dynamic cues to object shape , even in the absence of any contour relatability whatsoever .
A classification of VR-based methods is proposed : real-time rotoscopy methods , real-time direct metaphors and real-time recognition-based metaphors . Several examples are presented : 3D shape creation , camera motion , body motion control , hand animation , and facial animation . The hardware and software architecture of our animation system is also described .
We present multiresolution tree-structured networks to process point clouds for 3D shape understanding and generation tasks . Our network represents a 3D shape as a set of locality-preserving 1D ordered list of points at multiple resolutions .
Thorough reviews of the literature reveal a significant amount of publications concerning tactile human- machine interfaces , especially onwards the ~1990 . This paper reports the progress in tactile interfaces technology in the following areas : teleoperation , telepresence , sensory substitution , 3D surface generation , Braille systems , laboratory prototypes , and games . Different parameters and specifications required to generate and feed back tactile sensation are summarized .
In this paper is introduced an image guided oral implant system ( IGOIS ) , including the 3D surface-model generation through Marching Cubes algorithm and large-scale triangular mesh simplification , the realization of pre-operative planning module with computer graphics and image processing technology , the non-invasive point-to-point registration with the fabrication of tooth-supported polymer resin templates and ICP algorithm , and the development of the real-time navigation system software by programming in VTK and VC+ + 6.0 . The experimental test for a patient 's rapid prototype model shows that with the support of IGOIS , the precision achieved in the planning phase can be transferred to the patient so that the accuracy of the oral implant surgery under difficult conditions is improved .
We present a software package which uniquely integrates several facets of image-guided medicine into a single portable , extendable environment . It provides capabilities for automatic registration , semi-automatic segmentation , 3D surface model generation , 3D visualization , and quantitative analysis of various medical scans . We describe its system architecture , wide range of applications , and novel integration with an interventional Magnetic Resonance ( MR ) scanner to augment intra-operative imaging with pre-operative data .
The method was tested on two buildings . The program package Photomodeler version 5 was used , enabling the complete project accomplishment that includes camera calibration , image measurements , adjustments of object coordinates , generation of surface 3D models and photorendering . The phases of the project are described as well as limitations and problems encountered in practice .
This thesis discusses the application of the track function of geometric drawing board in 3-D surface drawing , and introduces the ways of drawing the surface under the " track function " based on the basic network , and drawing the surface under the " pursue function " by setting up three dimensional coordinate system .
We have developed a method to simulate complex thermal scenes under variable solar illumination , different materials and including gas plumes . The IDL-based scene simulation tool consists of public domain tools for : 3D geometry generation , raytracing , spectral and thermal property libraries , atmospheric transmission and emission modeling . The data can then be used in standard hyperspectral processing programs or processed by special purpose programs .
We have been developing Spherical Medial Representation ( SM-Rep ) based on skeletonisation over the sphere . While M-Rep provides a paradigm for 3D surface and image analysis , the resulting surface remains in a 3D vector space . Since the geometry of the unit sphere is quite different , especially geodesies , SM-Rep supplies consistent elements to overcome this issue .
Also another based on partial resampling is used to paste a 2.5D model to any other 3D model , and has made some good results . The work of this paper has greatly shortened the path from 2D editing to 3D surface animation .
In this paper , I propose the image-based 3D modeling system using calibrated camera . The proposed algorithm for rendering 3D model is consisted of three steps , camera calibration , 3D shape reconstruction and 3D surface generation step . In the camera calibration step , I estimate the camera matrix for the image aquisition camera .
Extensive pre-processing of the imagery was performed to prepare the data for this purpose . The steps incorporated for this purpose consist of 1 ) Data Calibration , 2 ) Mosaicking , 3 ) Pan Sharpening , 4 ) Generation of 3D surface , and 5 ) Digitization . Using image classification as the primary method of information extraction , this study tests the ever-popular pixel-based classification technique against the uprising object-based classification technique .
[ reference ] ) , i.e. a mesh , which is more desirable for many real applications since it is lightweight , capable of modelling shape details , easy to deform for animation , to name a few . In this paper , we push along the direction of single image reconstruction , and propose an algorithm to extract a 3D triangular mesh from a single color image . Rather than directly synthesizing , our model learns to deform a mesh from a mean shape to the target geometry .
Neuron models were optimized to reproduce electrophysiological properties of major classes of M1 neurons . Layer 5 corticospinal and corticostriatal neuron morphologies with 700 + compartments reproduced cell 3D reconstructions , and their ionic channel distributions were optimized within experimental constraints to reproduce in vitro recordings . The network was driven by the main long-range inputs to M1 : posterior nucleus ( PO ) and ventrolateral ( VL ) thalamus PO , primary and secondary somatosensory cortices ( S1 , S2 ) , contralateral M1 , secondary motor cortex ( M2 ) , and orbital cortex ( OC ) .
The methodology sets a library of modules that can be used to assemble a solid geometric LN model and subsequently generate an adaptive mesh model capable of implementing transport phenomena . Overall , based on the use of high-resolution confocal microscopy and morphological analysis of cell 3D reconstructions , we have developed a computational model of the LN geometry , suitable for further investigation in studies of fluid transport and cell migration in this immunologically essential organ .
Patterned illumination is a successful set of techniques in high resolution 3D microscopy . In particular , structured illumination microscopy is based on the projection of 1D periodic patterns onto the 3D sample under study .
High quality depth map estimation is required for better visualization of 3D views as there is great impact of depth map quality on overall 3D image quality . If the depth is estimated from conventional ways using two or more images , some defects come into picture , mostly in regions without texture .
” It gave them the idea for 3DNetMod , their new computational method to analyze 3D genome-folding . Topologically associating domains ( TADs ) are chromosomal compartments within which other TADs can be nested .
This poster deals with the problem of sensing virtual environments for 3D intelligent multi-character simulations . As these creatures should display reactive skills ( navigation or gazing ) , together with the necessary planning processes , required to animate their behaviours , we present an efficient and fully scalable sensor system designed to provide this information ( low level + high level ) to different kinds of 3D embodied agents ( games , storytelling , etc ) .
The lag between head motion and display response results in motion sickness , indisposition , and , at worst , abortion of the telepresence session . In this paper , we propose a delay compensation approach for 3D 360 ° telepresence systems realized with a mechanically actuated stereoscopic vision system . We further introduce a novel metric to evaluate the achievable level of delay compensation .
In this paper , a novel approach for creating 3D models of building scenes is presented . The proposed method is fully automated and fast , and accurately reconstructs both outdoor images of a building and indoor scenes , with perspective cues in real-time , using only one image .
Electronic product tags are the current standard for authentication purposes ; however , often this technology is neither secure nor cost-effective , and fails to take advantage of other unique 3D printing features . Considering the great usability of the QR code , we are motivated to enhance the QR code for the practical and cost-effective 3D printing product identification . Particularly , we bring up the all-in-one design , all-in-one manufacturing concept incorporating the QR code in the complete 3D printing paradigm .
The problem can be thought as a combination of three main approaches . ( 1 ) Image reconstruction of 3D artery volume from few number of projections ( each is presented as 2D image ) , ( 2 ) automatic detection of the catheter roadmap to the labeled artery which feed the tumor , and ( 3 ) interactive system to control and display images using simple gestures of the physician .
MDCT scanners have significantly improved the image quality by dramatically reducing artifacts resulting from partial volume averaging and motion . Additionally , fast and user-friendly scanner workstations allow for post-processing of impressive multiplanar 2D and 3D reconstructions , which are desirable to clinicians for comprehensive pre-surgical planning purposes [ 1 , 2 ] .
In , the 3DMM parameters are estimated in six steps each of which employs a different CNN . Notably , estimates the 3DMM parameters on a sparse set of landmarks , i.e. the purpose of is 3D face alignment rather than face reconstruction . The method of is currently considered the state - of - the - art in 3DMM fitting .
In this work , we introduce PoseCNN , a new Convolutional Neural Network for 6D object pose estimation . PoseCNN estimates the 3D translation of an object by localizing its center in the image and predicting its distance from the camera . The 3D rotation of the object is estimated by regressing to a quaternion representation .
section : Conclusion We introduced a viewpoint invariant model that estimates 3D human pose from a single depth image . Our model is formulated as a deep discriminative model that attends to glimpses in the input .
[ reference ] [ reference ] . subsection : 3D Image Rotation When the depth information is estimated , the face image can be rotated in 3D space to generate the appearances in larger poses ( Fig .
We therefore get the identities o of the visible object ( s ) , and for these objects , we use the segmentation centroids as their 2D centers , to compute the 3D poses of the objects as described below . section : Predicting the 3D Pose We predict the 3D pose of an object by applying a Deep Network to an image window W centered on the 2D object center estimated as described in the previous section .
This results in , where is the mean texture vector and is the orthonormal basis after keeping the first principal components . This model can be used to generate novel 3D feature - based texture instances with the function as where are the texture parameters . Finally , an iterative procedure is used in order to refine the texture .
The comparative simulation experimental results between our method and the traditional methods , such as least-squares and eigenvalue method , are provided . Moreover , our method is applied to the experiment of 3D laser scanning point cloud planes for the attitude estimation . The method is validated on both simulated data and real data .
3D Plane Detection and Reconstruction From a Single Image . This paper proposes a deep neural architecture , PlaneRCNN , that detects and reconstructs piecewise planar regions from a single RGB image .
Such an inter-linked framework provides a holistic approach to scene understanding , while obtaining the best possible detection rates . Motivated by recent research in coherent geometrical contextual reasoning and object recognition , this paper proposes a unified framework for robust 3D supporting plane estimation using a joint probabilistic model which uses results from object shape detection and 3D plane estimation . Maximization of the joint probabilistic model leads to robust 3D surface estimation while reducing false perceptual grouping .
The presented approach is suitable for the calibration of rigs composed by N cameras modelled by N different models . Calibration results , compared with state of the art approaches , and a 3D plane estimation application , allowed by the calibration , show the effectiveness of the approach . A cross-platform software implementing this method is available1 .
The algorithm 's operation is simple , adaptable and flexible . The algorithm is a new algorithm for 3D plane recognition , and the effectiveness of the algorithm has been verified by experiments .
Since the introduction of the Microsoft Kinect sensor , real-time 3D perception of objects in a close-range scene has become one of the major trends in the current research on robotics and computer vision domain . As part of a long-term goal to develop a robust object recognition system for industrial applications , this paper focuses on the research topic of 3D planes recognition and separation for polyhedral object grasping using industrial manipulators and Kinect as a 3D vision system . Since the data acquired by the vision sensor such as Kinect are invariably noisy , a pipeline of algorithms has been implemented to reduce the noise and clustering the data points properly .
However , it suffers from the spurious-plane problem when noise and outliers exist due to the uncertainty of randomly sampling the minimum subset with 3 points . An improved RANSAC method based on Normal Distribution Transformation ( NDT ) cells is proposed in this study to avoid spurious planes for 3D point-cloud plane segmentation . A planar NDT cell is selected as a minimal sample in each iteration to ensure the correctness of sampling on the same plane surface .
This paper presents a combined bottom-up and top-down approach to 3D roof plane detection and segmentation from laser scanning point clouds . Laser scanning data of city scenes often shows noise and incompleteness because of , e.g. , the clutter by trees and the reflection of windows/waterlogged depressions on the roof .
Learning to Infer Implicit Surfaces without 3D Supervision Learning Discriminative and Generative Shape Embeddings for Three-Dimensional Shape Retrieval Emphasizing 3D Properties in Recurrent Multi-View Aggregation for 3D Shape Retrieval Learning non-rigid, 3D shape variations using statistical, physical and geometric models Multiview normal field integration using level set methods Learning Part Generation and Assembly for Structure-aware Shape Synthesis Adaptive O-CNN Real-Time Line Drawing of 3D Models Taking into Account Curvature-Based Importance Haptic-constraint modeling based on interactive metaballs Dynamic Volume Completion and Deformation Using Virtual Reality Techniques in the Animation Process Multiresolution Tree Networks for 3D Point Cloud Processing Tactile interfaces: a state-of-the-art survey [Research on the development of image guided oral implant system]. An Integrated Visualization System for Surgical Planning and Guidance Using Image Fusion and Interventional Imaging Generation of metric building models using a low-cost tool On Track Function of Geometric Drawing Board in 3-D Surface Drawing Synthetic hyperspectral data cubes for complex thermal scenes Ball-and-socket joint motion description using spherical medial representation Surface Animation Based on 2D Editing A Image-based 3-D Shape Reconstruction using Pyramidal Volume Intersection COMPARISON OF PIXEL AND OBJECT-BASED CLASSIFICATION TECHNIQUES FOR GLACIER FACIES EXTRACTION Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images Long-range inputs and H-current regulate different modes of operation in a multiscale model of mouse M1 microcircuits Computational Approach to 3D Modeling of the Lymph Node Geometry Novel proposals in widefield 3D microscopy High quality depth map estimation by kinect upsampling and hole filling using RGB features and mutual information The Author File: Jennifer Phillips-Cremins An Efficient Synthetic Vision System for 3D Multi-character Systems Delay Compensation for Actuated Stereoscopic 360 Degree Telepresence Systems with Probabilistic Head Motion Prediction Automatic generation of 3D outdoor and indoor building scenes from a single image My Smartphone Recognizes Genuine QR Codes! Development of interactive 3D imaging system for hepatic angiography Volumetric Computed Tomography of the Tracheobronchial Tree Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes Towards Viewpoint Invariant 3D Human Pose Estimation Face Alignment Across Large Poses: A 3D Solution BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth 3D Face Morphable Models "In-the-Wild" Robust parameter estimation from point cloud data with noises for augmented reality PlaneRCNN: 3D Plane Detection and Reconstruction From a Single Image Combining Plane Estimation with Shape Detection for Holistic Scene Understanding Multiple camera types simultaneous stereo calibration A 3D Plane Recognition Algorithm Based on Image Matching Vision-based robotic system for polyhedral object grasping using kinect sensor An Improved RANSAC for 3D Point Cloud Plane Segmentation Based on Normal Distribution Transformation Cells Rule-based roof plane detection and segmentation from laser point clouds
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NeurIPS AAAI CVPR Virtual Reality Systems ECCV (7) MICCAI Edutainment ECCV NCVPRIPG IVA WACV 2017 IEEE International Conference on Computer Vision (ICCV) Robotics: Science and Systems ECCV 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE International Conference on Computer Vision (ICCV) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) CVPR ACIVS ICRA JURSE
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