We propose multi-view and volumetric convolutional neural networks ( ConvNets ) for 3D shape recognition , which combines surface normal and height fields to capture local geometry and physical size of an object . This strategy helps distinguishing between objects with similar geometries but different sizes . This paper addresses 3D shape recognition . Recent work typically represents a 3D shape as a set of binary variables corresponding to 3D voxels of a uniform 3D grid centered on the shape , and resorts to deep convolutional neural networks ( CNNs ) for modeling these binary variables . Three-dimensional ( 3D ) shape recognition has drawn much research attention in the field of computer vision . The advances of deep learning encourage various deep models for 3D feature representation . In particular , we focus on the generation of kernels when examples ( images , for instance ) are structured data ( i.e. described by sets of vectors ) modeled by Gamma mixtures . Experimental results on real-world challenging applications , namely 3D shape class recognition , object categorization , and video event analysis , show the effectiveness of the proposed framework . Force presented by each actuator was computed based on the simulation of contact between the virtual hand and object . In order to evaluate the performance of these devices , 3-dimensional shape recognition experiment of virtual objects was conducted . Measurement results of correct answer rate and response time were 65.6 % and 46.1 s in device1 . We introduce a novel method to expand the training set and optimize the architecture of the network . The experimental results show that our approach outperforms DeepPano and can deal with more complex 3D shape recognition problems with a higher diversity of target orientation . We introduce a new motion estimation framework for AUVs which relies on the successive acoustic images to infer AUV ego-motion . Also , we propose an Acoustic Stereo Imaging ( ASI ) system for underwater 3D reconstruction based on forward looking sonars ; the proposed system features cheaper implementation than planar array sonars and solves the delay problem in T configured 3D sonars . A one-shot sensor for underwater 3D reconstruction is presented and tested underwater in a water tank . The system is composed of a RGB CCD camera and a 532 nm green laser with a Diffractive Optical Element attached to it . Particles and impurities present in underwater produces Gaussian noise on the captured underwater optical images which degrades the quality of images . However , using depth sensors , as a cost effective alternative , we aim to show that underwater 3D scene reconstruction is possible with sight tradeoffs on accuracy but major cost saving . The acquired depth data is proposed to be processed by applying real-time mesh generating techniques from the acquired point cloud . In this paper we propose a robust , track before detect particle filtering algorithm for tracking the direction of arrival ( DOA ) of multiple echoes impinging on a sonar array . We also provide a real data application of our filter for underwater 3 dimensional ( 3-D ) scene reconstruction , i.e. bathymetry . Shallow water environments generally involve multi-path propagation channels . Simulated data results showcase the improved performance of the proposed filter . Results on real sonar phased-array data are presented for underwater 3D image reconstruction applications . This paper deals with a novel 6D trajectory planning approach for Autonomous Underwater Vehicle ( AUV ) that is performing Underwater Inspection Mission ( UIM ) . AUV has attached SONAR system and is used for underwater 3D object surface reconstruction . Flat 3D CAD surface models and complex 3D CAD ship ’s hull models are used for trajectory planning around them . In this study , a laser line auto-scanning system was designed to perform underwater close-range 3D reconstructions with high accuracy and resolution . The system changes the laser plane direction with a galvanometer to perform automatic scanning and obtain continuous laser strips for underwater 3D reconstruction . The refraction of light occurs when a light ray passes through the water-glass and air-glass interface , rendering the conventional multiple view geometry based on the single viewpoint ( SVP ) camera model invalid . While most recent underwater vision studies mainly focus on the challenging topic of calibrating such systems , no previous work has systematically studied the influence of refraction on underwater three-dimensional ( 3D ) reconstruction . This paper demonstrates the possibility of using the SVP camera model in underwater 3D reconstruction through theoretical analysis of refractive distortion and simulations . The study is related to our research project Cyclope , this embedded sensor based on active stereo-vision approach allows real time 3D objects reconstruction . Our medical application requires differentiation between hyperplastic and adenomatous polyps during 3D endoscopic imaging . The detection algorithm consists of SVM classifier trained on robust feature descriptors of a surfacic 3D point cloud extracted from the surface of studied object . In recent years , 3D endoscopic imaging has played an important role in the development of the medical industry , making it easy for medical doctors to judge the visual depth of ulcers or tumors and thereby decreasing the risk of unnecessary damages to other tissues . A traditional 2D endoscopic image can be converted into a 3D view through depth estimation and the use of a depth image-based rendering method . This chapter begins with an overview of techniques for thermal via insertion . Next , it addresses the problem of 3D routing , overcoming challenges as conventional 2D routing is stretched to a third dimension and as electrical routes must vie with thermal vias for scarce on-chip routing resources , particularly intertier vias . The focus of this paper lies on the creation of 3D hu- man skeleton from a set of 2D images . Unlike available approaches , which utilize a single 2D image for 3D recon- struction , the prosecuted approach utilities a set of multi- ple images , which are obtained from a simple query to the google image database . We only assume , that a query key- word can be linked to a set of images , which contain a rep- resentative subset related to the query . Third , in order to improve the quality of the reconstruction pro- cess , an automatic best reference slice selection algorithm is developed based on an iterative assessment of image entropy and mean square error of the registration process . Finally , we demonstrate that the choice of the reference slice has a signif- icant impact on registration quality and subsequent 3D recon- struction . In this paper we introduce a new point cloud registration strategy for 3D surface inspection , named object-oriented method . Traditionally , the registration methods use a fitting based algorithm , which is a recursive process and too time consuming to do quality inspection . We build a low-cost mammography workstation to detect and classify clusters of microcalcifications and tissue densities automatically . New in this approach is the estimation of the 3D formation of segmented microcalcifications and its visualization which will put additional diagnostic information at the radiologists disposal . The real problem using only two or three projections for reconstruction is the big loss of volume information . This paper proposes a novel approach for automatically controlling stereoscopic camera parameters that specifically addresses challenges in stereo 3D animation production process . Our proposed camera control method produces stereo contents with preferable depth perception and guarantees visual comfort by optimization of camera parameters . Existing interaction techniques for mobile AR often use the multi-touch capabilities of the device 's display for object selection and manipulation . To provide full 3D manipulation by touch in an integral way , existing approaches use complex multi finger and hand gestures . However , they are difficult or impossible to use in one-handed handheld AR scenarios and their usage requires prior knowledge . The proposed approach allows the data of 3D mesh to be transmitted over wireless network using TCP and UDP protocols respectively . It speeds up 3D graphics browsing on mobile devices as well as optimizes the data size of 3D mesh for progressive transmission . Finally , the 3D imaging of targets is reconstructed . Simulation results show that this method can effectively realize the 3D imaging of the rotating space targets even though the radar pulse repetition frequency ( PRF ) is insufficient . In addition , compared with the single measurement vector model ( SMV ) , the MMV-based method performs better in robustness . By contrast , unstructured-grid-based methods can model complex underground structures with high accuracy and overcome the defects of traditional methods , such as the high computational cost for improving model accuracy and the difficulty of inverting with topography . In this paper , we used the limited-memory quasi-Newton ( L-BFGS ) method with an unstructured finite-element grid to perform 3D MT inversions . This method avoids explicitly calculating Hessian matrices , which greatly reduces the memory requirements . However , 2D face alignment only regresses visible landmarks on faces , which are unable to address large pose or occlusion situations , where partial face regions are invisible . With the development of this field , 3D face alignment have been proposed , aiming to fit a 3DMM [ reference ][ reference ][ reference ] or register a 3D facial template [ reference ][ reference ] to a 2D face image , which makes it possible to deal with the invisible points . The original 3DMM fitting method [ reference ] fits the 3D model by minimizing the pixel - wise difference between image and the rendered face model . Our results show that ( a ) better landmark detection accuracy measured on the 300W benchmark does not necessarily imply better face recognition accuracy . ( b ) Our FPN provides superior 2D and 3D face alignment on both benchmarks . Finally , ( c ) , FPN aligns faces at a small fraction of the computational cost of comparably accurate landmark detectors . Stronger shape priors are missing . Recently , large - scale repositories of 3D CAD models , such as ShapeNet [ reference ] , have been introduced . They have great potential for 3D reconstruction tasks . Following this key study , researchers have tried applying 3D ConvNets , autoencoders , and a variety of probabilistic neural generative models to the problem of 3D model recognition , with each study progressively advancing state - of - the - art . With respect to 3D object generation from 2D images , commonly used methods can be roughly grouped into two categories : 3D voxel prediction and mesh - based methods . The 3D - R2N2 model represents a more recent approach to the task , which involves training a recurrent neural network to predict 3D voxels from one or more 2D images . 3D box regression from depth data Newer studies have proposed to directly tackle the 3D object detection problem in discretized 3D spaces . Song learn to classify 3D bounding box proposals generated by a 3D sliding window using synthetically - generated 3D features . A follow - up study uses a 3D variant of the Region Proposal Network to generate 3D proposals and uses a 3D ConvNet to process the voxelized point cloud . section : Can 3DMatch generalize to new domains ? As a final test , we evaluate the ability of our 3DMatch descriptor , which is trained from 3D reconstructions , to generalize to completely different tasks and spatial scales ; namely , 6D object pose estimation by model alignment and correspondence labeling for 3D meshes . section : 6D section : Closely related work This section reviews closely related work in 3D face reconstruction , depth estimation using CNNs and work on 3D representation modelling with CNNs . 3D face reconstruction . Train on frontal views , test on top views . This is the most difficult task for 3D pose estimation algorithms since the test set contains significant scale and shape differences from the training data . Results are shown in Table [ reference ] . The article deals with the subjective evaluation of 3D image perception for different viewing angles . Results of the subjective testing , observations of the two different 3D video-sequences and two 3D photographs made by 28 observers on two different types of 3D displays ( active and passive technologies ) are shown . This paper introduces the use of stereoscopic images in Explosive Ordnance Disposal(EOD ) tasks . A 3D image perception is a key in the ability to manipulate in remote environments . By using two video cameras and a special stereoscopic shuttering glass , three dimensional images can be displayed on a CRT monitor for assisting manipulation tasks . A laminar cortical model of stereopsis and 3D surface perception is developed and simulated . The model shows how spiking neurons that interact in hierarchically organized laminar circuits of the vi ... A laminar cortical model of stereopsis and 3D surface perception is developed and simulated . The model shows how spiking neurons that interact in hierarchically organized laminar circuits of the visual cortex can generate analog properties of 3D visual percepts . Depth estimation from a single image plays an important role in 3D scene perception . Owing to the development of deep convolutional neural networks ( CNNs ) , monocular depth estimation models have achieved a large number of exciting results . Abstract Depth estimation is a fundamental task for 3D scene perception . Unsupervised learning is a prevalent method for depth estimation due to its generalization ability , and it requires no extra ground truth of depth for training . In recent years , Deep Learning based method for 3-Dimension ( 3D ) geometry perception tasks , like dense depth recovery , optical flow estimation and ego-motion estimation , is attracting significant attention . Inspired by recent advances in unsupervised strategies to learning from video datasets , we present a reasonable combination of constrains and a finer architecture , used for unsupervised ego-motion and depth estimation . Abstract Early augmented reality ( AR ) community has paid a lot of attention to identifying 2D fiducial markers in AR scene , only a little works have been devoted to 3D objects perception . With the massively increased usage of 3D representation , it is highly desirable that powerful processing tools and algorithms should be developed for 3D recognition purpose in the augmented reality domain . Electrovibration technology has the potential for seamless integration into ordinary smartphones and tablets to provide programmable haptic feedback . The aim of this work is to seek effective ways to improve 3D perception of visual objects rendered on an electrovibration display . Utilizing a gradient-based algorithm , we first investigated whether rendering only lateral frictional force on an electrovibration display improves 3D shape perception compared to doing the same using a force-feedback interface . We can understand this task as involving several other problems , such as object detection , pose estimation , part and instance segmentation either as special cases or prerequisites . Addressing this task has applications in problems that require going beyond plain landmark localization , such as graphics , augmented reality , or human - computer interaction , and could also be a stepping stone towards general 3D - based object understanding . The task of establishing dense correspondences from an image to a surface - based model has been addressed mostly in the setting where a depth sensor is available , as in the Vitruvian manifold of , metric regression forests , or the more recent dense point cloud correspondence of . Furthermore , they enshrine quantization artifacts that obscure natural invariances of the data under rigid motions , etc . In this paper we address the problem of generating the 3D geometry of an object based on a single image of that object . We explore generative networks for 3D geometry based on a point cloud representation . With the popularity of 3D sensors deployed on mobile devices and autonomous vehicles , more and more 3D data is captured and processed . In this work , we study one of the most important 3D perception tasks – 3D object detection , which classifies the object category and estimates oriented 3D bounding boxes of physical objects from 3D sensor data . In Table [ reference ] we compare our method against the recently introduced SSD6D and other methods on the LineMOD dataset . SSD6D also trains on synthetic views of 3D models , but their performance seems quite dependent on a sophisticated occlusion - aware , projective ICP refinement step . Our basic ICP sometimes converges to similarly shaped objects in the vicinity .
We propose multi-view and volumetric convolutional neural networks ( ConvNets ) for 3D shape recognition , which combines surface normal and height fields to capture local geometry and physical size of an object . This strategy helps distinguishing between objects with similar geometries but different sizes .
This paper addresses 3D shape recognition . Recent work typically represents a 3D shape as a set of binary variables corresponding to 3D voxels of a uniform 3D grid centered on the shape , and resorts to deep convolutional neural networks ( CNNs ) for modeling these binary variables .
Three-dimensional ( 3D ) shape recognition has drawn much research attention in the field of computer vision . The advances of deep learning encourage various deep models for 3D feature representation .
In particular , we focus on the generation of kernels when examples ( images , for instance ) are structured data ( i.e. described by sets of vectors ) modeled by Gamma mixtures . Experimental results on real-world challenging applications , namely 3D shape class recognition , object categorization , and video event analysis , show the effectiveness of the proposed framework .
Force presented by each actuator was computed based on the simulation of contact between the virtual hand and object . In order to evaluate the performance of these devices , 3-dimensional shape recognition experiment of virtual objects was conducted . Measurement results of correct answer rate and response time were 65.6 % and 46.1 s in device1 .
We introduce a novel method to expand the training set and optimize the architecture of the network . The experimental results show that our approach outperforms DeepPano and can deal with more complex 3D shape recognition problems with a higher diversity of target orientation .
We introduce a new motion estimation framework for AUVs which relies on the successive acoustic images to infer AUV ego-motion . Also , we propose an Acoustic Stereo Imaging ( ASI ) system for underwater 3D reconstruction based on forward looking sonars ; the proposed system features cheaper implementation than planar array sonars and solves the delay problem in T configured 3D sonars .
A one-shot sensor for underwater 3D reconstruction is presented and tested underwater in a water tank . The system is composed of a RGB CCD camera and a 532 nm green laser with a Diffractive Optical Element attached to it .
Particles and impurities present in underwater produces Gaussian noise on the captured underwater optical images which degrades the quality of images . However , using depth sensors , as a cost effective alternative , we aim to show that underwater 3D scene reconstruction is possible with sight tradeoffs on accuracy but major cost saving . The acquired depth data is proposed to be processed by applying real-time mesh generating techniques from the acquired point cloud .
In this paper we propose a robust , track before detect particle filtering algorithm for tracking the direction of arrival ( DOA ) of multiple echoes impinging on a sonar array . We also provide a real data application of our filter for underwater 3 dimensional ( 3-D ) scene reconstruction , i.e. bathymetry . Shallow water environments generally involve multi-path propagation channels .
Simulated data results showcase the improved performance of the proposed filter . Results on real sonar phased-array data are presented for underwater 3D image reconstruction applications .
This paper deals with a novel 6D trajectory planning approach for Autonomous Underwater Vehicle ( AUV ) that is performing Underwater Inspection Mission ( UIM ) . AUV has attached SONAR system and is used for underwater 3D object surface reconstruction . Flat 3D CAD surface models and complex 3D CAD ship ’s hull models are used for trajectory planning around them .
In this study , a laser line auto-scanning system was designed to perform underwater close-range 3D reconstructions with high accuracy and resolution . The system changes the laser plane direction with a galvanometer to perform automatic scanning and obtain continuous laser strips for underwater 3D reconstruction .
The refraction of light occurs when a light ray passes through the water-glass and air-glass interface , rendering the conventional multiple view geometry based on the single viewpoint ( SVP ) camera model invalid . While most recent underwater vision studies mainly focus on the challenging topic of calibrating such systems , no previous work has systematically studied the influence of refraction on underwater three-dimensional ( 3D ) reconstruction . This paper demonstrates the possibility of using the SVP camera model in underwater 3D reconstruction through theoretical analysis of refractive distortion and simulations .
The study is related to our research project Cyclope , this embedded sensor based on active stereo-vision approach allows real time 3D objects reconstruction . Our medical application requires differentiation between hyperplastic and adenomatous polyps during 3D endoscopic imaging . The detection algorithm consists of SVM classifier trained on robust feature descriptors of a surfacic 3D point cloud extracted from the surface of studied object .
In recent years , 3D endoscopic imaging has played an important role in the development of the medical industry , making it easy for medical doctors to judge the visual depth of ulcers or tumors and thereby decreasing the risk of unnecessary damages to other tissues . A traditional 2D endoscopic image can be converted into a 3D view through depth estimation and the use of a depth image-based rendering method .
This chapter begins with an overview of techniques for thermal via insertion . Next , it addresses the problem of 3D routing , overcoming challenges as conventional 2D routing is stretched to a third dimension and as electrical routes must vie with thermal vias for scarce on-chip routing resources , particularly intertier vias .
The focus of this paper lies on the creation of 3D hu- man skeleton from a set of 2D images . Unlike available approaches , which utilize a single 2D image for 3D recon- struction , the prosecuted approach utilities a set of multi- ple images , which are obtained from a simple query to the google image database . We only assume , that a query key- word can be linked to a set of images , which contain a rep- resentative subset related to the query .
Third , in order to improve the quality of the reconstruction pro- cess , an automatic best reference slice selection algorithm is developed based on an iterative assessment of image entropy and mean square error of the registration process . Finally , we demonstrate that the choice of the reference slice has a signif- icant impact on registration quality and subsequent 3D recon- struction .
In this paper we introduce a new point cloud registration strategy for 3D surface inspection , named object-oriented method . Traditionally , the registration methods use a fitting based algorithm , which is a recursive process and too time consuming to do quality inspection .
We build a low-cost mammography workstation to detect and classify clusters of microcalcifications and tissue densities automatically . New in this approach is the estimation of the 3D formation of segmented microcalcifications and its visualization which will put additional diagnostic information at the radiologists disposal . The real problem using only two or three projections for reconstruction is the big loss of volume information .
This paper proposes a novel approach for automatically controlling stereoscopic camera parameters that specifically addresses challenges in stereo 3D animation production process . Our proposed camera control method produces stereo contents with preferable depth perception and guarantees visual comfort by optimization of camera parameters .
Existing interaction techniques for mobile AR often use the multi-touch capabilities of the device 's display for object selection and manipulation . To provide full 3D manipulation by touch in an integral way , existing approaches use complex multi finger and hand gestures . However , they are difficult or impossible to use in one-handed handheld AR scenarios and their usage requires prior knowledge .
The proposed approach allows the data of 3D mesh to be transmitted over wireless network using TCP and UDP protocols respectively . It speeds up 3D graphics browsing on mobile devices as well as optimizes the data size of 3D mesh for progressive transmission .
Finally , the 3D imaging of targets is reconstructed . Simulation results show that this method can effectively realize the 3D imaging of the rotating space targets even though the radar pulse repetition frequency ( PRF ) is insufficient . In addition , compared with the single measurement vector model ( SMV ) , the MMV-based method performs better in robustness .
By contrast , unstructured-grid-based methods can model complex underground structures with high accuracy and overcome the defects of traditional methods , such as the high computational cost for improving model accuracy and the difficulty of inverting with topography . In this paper , we used the limited-memory quasi-Newton ( L-BFGS ) method with an unstructured finite-element grid to perform 3D MT inversions . This method avoids explicitly calculating Hessian matrices , which greatly reduces the memory requirements .
However , 2D face alignment only regresses visible landmarks on faces , which are unable to address large pose or occlusion situations , where partial face regions are invisible . With the development of this field , 3D face alignment have been proposed , aiming to fit a 3DMM [ reference ][ reference ][ reference ] or register a 3D facial template [ reference ][ reference ] to a 2D face image , which makes it possible to deal with the invisible points . The original 3DMM fitting method [ reference ] fits the 3D model by minimizing the pixel - wise difference between image and the rendered face model .
Our results show that ( a ) better landmark detection accuracy measured on the 300W benchmark does not necessarily imply better face recognition accuracy . ( b ) Our FPN provides superior 2D and 3D face alignment on both benchmarks . Finally , ( c ) , FPN aligns faces at a small fraction of the computational cost of comparably accurate landmark detectors .
Stronger shape priors are missing . Recently , large - scale repositories of 3D CAD models , such as ShapeNet [ reference ] , have been introduced . They have great potential for 3D reconstruction tasks .
Following this key study , researchers have tried applying 3D ConvNets , autoencoders , and a variety of probabilistic neural generative models to the problem of 3D model recognition , with each study progressively advancing state - of - the - art . With respect to 3D object generation from 2D images , commonly used methods can be roughly grouped into two categories : 3D voxel prediction and mesh - based methods . The 3D - R2N2 model represents a more recent approach to the task , which involves training a recurrent neural network to predict 3D voxels from one or more 2D images .
3D box regression from depth data Newer studies have proposed to directly tackle the 3D object detection problem in discretized 3D spaces . Song learn to classify 3D bounding box proposals generated by a 3D sliding window using synthetically - generated 3D features . A follow - up study uses a 3D variant of the Region Proposal Network to generate 3D proposals and uses a 3D ConvNet to process the voxelized point cloud .
section : Can 3DMatch generalize to new domains ? As a final test , we evaluate the ability of our 3DMatch descriptor , which is trained from 3D reconstructions , to generalize to completely different tasks and spatial scales ; namely , 6D object pose estimation by model alignment and correspondence labeling for 3D meshes . section : 6D
section : Closely related work This section reviews closely related work in 3D face reconstruction , depth estimation using CNNs and work on 3D representation modelling with CNNs . 3D face reconstruction .
Train on frontal views , test on top views . This is the most difficult task for 3D pose estimation algorithms since the test set contains significant scale and shape differences from the training data . Results are shown in Table [ reference ] .
The article deals with the subjective evaluation of 3D image perception for different viewing angles . Results of the subjective testing , observations of the two different 3D video-sequences and two 3D photographs made by 28 observers on two different types of 3D displays ( active and passive technologies ) are shown .
This paper introduces the use of stereoscopic images in Explosive Ordnance Disposal(EOD ) tasks . A 3D image perception is a key in the ability to manipulate in remote environments . By using two video cameras and a special stereoscopic shuttering glass , three dimensional images can be displayed on a CRT monitor for assisting manipulation tasks .
A laminar cortical model of stereopsis and 3D surface perception is developed and simulated . The model shows how spiking neurons that interact in hierarchically organized laminar circuits of the vi ...
A laminar cortical model of stereopsis and 3D surface perception is developed and simulated . The model shows how spiking neurons that interact in hierarchically organized laminar circuits of the visual cortex can generate analog properties of 3D visual percepts .
Depth estimation from a single image plays an important role in 3D scene perception . Owing to the development of deep convolutional neural networks ( CNNs ) , monocular depth estimation models have achieved a large number of exciting results .
Abstract Depth estimation is a fundamental task for 3D scene perception . Unsupervised learning is a prevalent method for depth estimation due to its generalization ability , and it requires no extra ground truth of depth for training .
In recent years , Deep Learning based method for 3-Dimension ( 3D ) geometry perception tasks , like dense depth recovery , optical flow estimation and ego-motion estimation , is attracting significant attention . Inspired by recent advances in unsupervised strategies to learning from video datasets , we present a reasonable combination of constrains and a finer architecture , used for unsupervised ego-motion and depth estimation .
Abstract Early augmented reality ( AR ) community has paid a lot of attention to identifying 2D fiducial markers in AR scene , only a little works have been devoted to 3D objects perception . With the massively increased usage of 3D representation , it is highly desirable that powerful processing tools and algorithms should be developed for 3D recognition purpose in the augmented reality domain .
Electrovibration technology has the potential for seamless integration into ordinary smartphones and tablets to provide programmable haptic feedback . The aim of this work is to seek effective ways to improve 3D perception of visual objects rendered on an electrovibration display . Utilizing a gradient-based algorithm , we first investigated whether rendering only lateral frictional force on an electrovibration display improves 3D shape perception compared to doing the same using a force-feedback interface .
We can understand this task as involving several other problems , such as object detection , pose estimation , part and instance segmentation either as special cases or prerequisites . Addressing this task has applications in problems that require going beyond plain landmark localization , such as graphics , augmented reality , or human - computer interaction , and could also be a stepping stone towards general 3D - based object understanding . The task of establishing dense correspondences from an image to a surface - based model has been addressed mostly in the setting where a depth sensor is available , as in the Vitruvian manifold of , metric regression forests , or the more recent dense point cloud correspondence of .
Furthermore , they enshrine quantization artifacts that obscure natural invariances of the data under rigid motions , etc . In this paper we address the problem of generating the 3D geometry of an object based on a single image of that object . We explore generative networks for 3D geometry based on a point cloud representation .
With the popularity of 3D sensors deployed on mobile devices and autonomous vehicles , more and more 3D data is captured and processed . In this work , we study one of the most important 3D perception tasks – 3D object detection , which classifies the object category and estimates oriented 3D bounding boxes of physical objects from 3D sensor data .
In Table [ reference ] we compare our method against the recently introduced SSD6D and other methods on the LineMOD dataset . SSD6D also trains on synthetic views of 3D models , but their performance seems quite dependent on a sophisticated occlusion - aware , projective ICP refinement step . Our basic ICP sometimes converges to similarly shaped objects in the vicinity .
3D convolutional neural networks by modal fusion Beam search for learning a deep Convolutional Neural Network of 3D shapes PVRNet: Point-View Relation Neural Network for 3D Shape Recognition A Finite Gamma Mixture Model-Based Discriminative Learning Frameworks Multi-point Pressure Sensation Display Using Pneumatic Actuators An Improved 3D Shape Recognition Method Based on Panoramic View 3D reconstruction and motion estimation using forward looking sonar One-shot underwater 3D reconstruction Underwater online 3D mapping and scene reconstruction using low cost kinect RGB-D sensor Robust, track before detect particle filter for bathymetric sonar application CPHD-DOA Tracking of Multiple Extended Sonar Targets in Impulsive Environments Trajectory Planning Based on 3D CAD models for Autonomous Underwater Vehicle Inspection A Laser Line Auto-Scanning System for Underwater 3D Reconstruction Experimental study of the influence of refraction on underwater three-dimensional reconstruction using the SVP camera model. 3-D object recognition based on SVM and stereo-vision: Application in endoscopic imaging A 3D Endoscopic Imaging System With Content-Adaptive Filtering and Hierarchical Similarity Analysis Thermal Via Insertion and Thermally Aware Routing in 3D ICs Reconstructing 3D Human Poses from Keyword Based Image Database Query Imgelerinin Tam Otomatik Geri Catilmasi Fully Automatic 3D Reconstruction of Histological Images Object-orientated registration method for surface inspection of automotive windshields Three-dimensional reconstruction of clustered microcalcifications from two digitized mammograms Automatically Stereoscopic Camera Control for 3D Animation Production 3DTouch and HOMER-S: intuitive manipulation techniques for one-handed handheld augmented reality Mobile 3D graphics compression for progressive transmission over wireless network Three-dimensional Interferometric Imaging of Space Rotating Targets in Low-resolution Radar based on Multiple Measurement Vectors Model 3D magnetotelluric inversions with unstructured finite-element and limited-memory quasi-Newton methods Joint 3D Face Reconstruction and Dense Face Alignment from A Single Image with 2D-Assisted Self-Supervised Learning FacePoseNet: Making a Case for Landmark-Free Face Alignment A Point Set Generation Network for 3D Object Reconstruction from a Single Image Learning a Hierarchical Latent-Variable Model of 3D Shapes PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression Towards Viewpoint Invariant 3D Human Pose Estimation Influence of the viewing angle on the spatial perception for various 3D displays A control of a bomb disposal robot using a stereoscopic vision (non-reviewed) Stereopsis and 3D surface perception by spiking neurons in laminar cortical circuits Stereopsis and 3D surface perception by spiking neurons in laminar cortical circuits: A method for converting neural rate models into spiking models An Adaptive Unsupervised Learning Framework for Monocular Depth Estimation Unsupervised learning of depth estimation based on attention model and global pose optimization Unsupervised Ego-Motion and Dense Depth Estimation with Monocular Video 3D registration based perception in augmented reality environment Improving 3D Shape Recognition withElectrostatic Friction Display DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild A Point Set Generation Network for 3D Object Reconstruction from a Single Image Frustum PointNets for 3D Object Detection from RGB-D Data Implicit 3D Orientation Learning for 6D Object Detection from RGB Images
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ICIP ICPR ICMLA EuroHaptics (2) ETFA FUSION SoCPaR 3DV IROS Medical Imaging: Image Processing ACM Multimedia VRIC CAD/Graphics ArXiv 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 International Conference on 3D Vision (3DV) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE International Conference on Computer Vision (ICCV) ECCV 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ECCV
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