Lidar has become an essential sensor for autonomous driving as it provides reliable depth estimation . Lidar is also the primary sensor used in building 3D maps which can be used even in the case of low-cost systems which do not use Lidar . Each camera is calibrated separately and afterwards a stereo calibration is performed . Depth estimation is calculated using two different algorithms and an evaluation of their performance is presented . The mobile devices handle the computational load fast enough to be able to produce the results within the walk time at normal pace . The most challenging issue in the field of visual control system is the accurate estimation of depth upon which all other parameters of pose estimation may depend . Here we have introduced a new approach for the depth estimation problem of a visual control system with a moving camera and stationary target . A unique visual pattern is designed to achieve the desired depth of the vision based system . Although deep neural networks have been widely applied to computer vision problems , extending them into multiview depth estimation is non-trivial . In this paper , we present MVDepthNet , a convolutional network to solve the depth estimation problem given several image-pose pairs from a localized monocular camera in neighbor viewpoints . Multiview observations are encoded in a cost volume and then combined with the reference image to estimate the depth map using an encoder-decoder network . However , the traditional maximum-likelihood based imaging method needs a long acquisition time to capture clear 3-D image at low light levels . To tackle this problem , we present a novel imaging method for depth estimate , which can obtain the accurate 3-D image in a short acquisition time . Our method combines the photon-count statistics with the temporal correlations of the reflected signal . Intermediate supervision is used in several outputs of the proposed architecture in a staged approach . To aid the process of training and inference , hand segmentation masks are also estimated in such an intermediate supervision step , and used to guide the subsequent depth estimation process . In order to train and evaluate the proposed method we compile and make publicly available HandRGBD , a new dataset of 20,601 views of hands , each consisting of an RGB image and an aligned depth map . After segmenting an image into planes from extracted line segments , data cost function and smoothness cost function for graph cut optimization is defined with the consideration of directions of line segments and neighborhood segments . Further , segments having same depth are grouped during depth estimation step using minimum spanning tree algorithm with proposed weights . Experimental result shows that the proposed method is not only able to identify complex Manhattan structures of indoor and outdoor scene but also provide exact boundaries and intersections of planes unlike earlier literature . First , for the recovery of AIF video , the sparse prior of natural video is incorporated to ensure a high-quality defocus blur removal even under inaccurate depth estimation . Second , in depth estimation step , we feed back the spatial and temporal constraints from the high-quality AIF video and adopt a numerical solution , which is robust to the inaccuracy of AIF recovery to further boost the performance of depth from the defocus algorithm . Benefitting from the incorporation of AIF video priors and the temporal consistency constraint , the proposed framework can effectively reconstruct the depth of the textureless region and is insensitive to camera parameter changes . For the problem of depth information estimation of micro vision image , a defocus characteristic parameter model based on Markov random field(MRF)is presented , which converts the depth information estimation problem of defocus characteristics into the optimization problem of energy function . With iterated conditional mode(ICM)algorithm applied to fulfill optimization and least squares estimate(LSE)algorithm employed to estimate the parameters of initial point , performance of the ICM algorithm is improved and the result of the algorithm is prevented from getting into local optimum . Taking the advantage of motion vector which reflects the movement between neighboring frames , the depth map could be reconstructed . The motion vector calculated by traditional motion estimation algorithm is not appropriate for depth information estimation and the computational requirement is huge . So we introduce the parameter of mutual information ( MI ) . 2D-to-3D conversion is one of the key technologies in stereoscopic video . Its visual qualities depend on the qualities of depth image estimation and virtual viewpoint image rendering . Although there are many methods to get depth information , the depth information still has some errors because of calculation errors and noises , which influences the visual qualities of the generated virtual viewpoint image . Inspired by the CS theory , this paper formulates the task of depth channel reconstruction as a sparsity-regularized least squares optimization problem . To solve this problem efficiently , an iterative algorithm based on the accelerated proximal gradient technique is developed , which not only speeds up the convergence rate , but also enhances the quality of depth image estimation . Several experiments are conducted and the results confirm the efficiency of the proposed approach . In this paper , we propose a discriminative learning-based method for recovering the depth of a scene from multiple defocused images . The proposed method consists of a discriminative learning phase and a depth estimation phase . In the discriminative learning phase , we formalize depth from defocus ( DFD ) as a multi-class classification problem which can be solved by learning the discriminative metrics from the synthetic training set by minimizing a criterion function . To enhance the discriminative and generalization performance of the learned metrics , the criterion takes both within-class and between-class variations into account , and incorporates margin constraints . In the depth estimation phase , for each pixel , we compute the N discriminative functions and determine the depth level according to the minimum discriminant value . Experimental results on synthetic and real images show the effectiveness of our method in providing a reliable estimation of the depth of a scene . For instance , matrix multiplication is used for feature covariance and covariance prediction computations . The computation takes more processing time when the number of features applied to the depth estimation calculation is increased . Therefore , there is a need to design a fast matrix multiplication to accelerate the processing speed . In the first approach , the sharpness of objects in the refocused images is evaluated using the FFT method , from which an empirical formula can be obtained to estimate the depth of objects . The second method uses the raw data in sub-images to calculate an index , Average Absolute Difference ( AAD ) , for depth estimation purposes . The advantages and disadvantages of two proposed approaches are discussed and compared . 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 . We further prove that optimizing this upper bound yields a Pareto optimal solution under realistic assumptions . We apply our method to a variety of multi - task deep learning problems including digit classification , scene understanding ( joint semantic segmentation , instance segmentation , and depth estimation ) , and multilabel classification . Our method produces higher - performing models than recent multi - task learning formulations or per - task training . It is the champion of ImageNet scene parsing challenge 2016 , and arrived the 1st place on PASCAL VOC 2012 semantic segmentation benchmark , and the 1st place on urban scene Cityscapes data . They manifest that PSPNet gives a promising direction for pixel - level prediction tasks , which may even benefit CNN - based stereo matching , optical flow , depth estimation , etc . in follow - up work . Our main contributions are threefold . While recent advances in convolutional neural networks have enabled accurate 2D detection in complex environments , the 3D object detection problem still remains an open challenge . Methods for 3D box regression from a single image , even including recent deep learning methods such as , still have relatively low accuracy especially in depth estimates at longer ranges . Hence , many current real - world systems either use stereo or augment their sensor stack with lidar and radar . subsection : 3D Image Meshing The depth estimation of a face image can be conducted on the face region and external region respectively , with different requirements of accuracy . On the face region , we fit a 3DMM through the Multi - Features Framework ( MFF ) , see Fig . Monocular depth estimation is a foundation task of three-dimensional ( 3D ) reconstruction which is used to improve the accuracy of environment perception . Because of the simpler hardware requirement , it is more suitable than other multi-view methods . Deep Learning based depth mapping has provided appreciable results for shorter ranges , but this method remains unexplored for longer ranges . A Convolutional Neural Networks(CNN ) based approach fares equally well with the other methods of monocular depth estimation such as linear regression , support vector machine based regression , decision tree regression by treating the range estimation problem as a regression problem . A comparison has been done among various methods that have been used to approach the problem . At present , deep learning has been applied more and more in monocular image depth estimation and has shown promising results . The current more ideal method for monocular depth estimation is the supervised learning based on ground truth depth , but this method requires an abundance of expensive ground truth depth as the supervised labels . In the research of monocular image depth estimation , the fully convolutional residual network structure can achieve better prediction results , but the precision of image depth prediction still has room for improvement . Therefore , this paper proposes a feature of enhanced fully convolutional networks ( FE-FCN ) . Recent models using deep neural networks have improved monocular depth estimation performance , but there is still difficulty in predicting absolute depth and generalizing outside a standard dataset . Here we introduce the paradigm of deep optics , i.e. end-to-end design of optics and image processing , to the monocular depth estimation problem , using coded defocus blur as an additional depth cue to be decoded by a neural network . We evaluate several optical coding strategies along with an end-to-end optimization scheme for depth estimation on three datasets , including NYU Depth v2 and KITTI . This either leads to suboptimal results or the demand of huge amount of expensive ground truth labelled data to generate reasonable results . In this paper , we show for the first time that the monocular depth estimation problem can be reformulated as two sub-problems , a view synthesis procedure followed by stereo matching , with two intriguing properties , namely i ) geometrical constraints can be explicitly imposed during inference ; ii ) demand on labelled depth data can be greatly alleviated . We show that the whole pipeline can still be trained in an end-to-end fashion and this new formulation plays a critical role in advancing the performance . Qualitatively , the estimated depth and renderings closely resemble the ground truth . ConclusionsThe proposed method shows a novel architecture to perform real-time monocular depth estimation without losing patient specificity in bronchoscopy . Future work will include integration within SLAM systems and collection of in vivo datasets . Our proposed network , FastDepth , runs at 178 fps on an NVIDIA Jetson TX2 GPU and at 27 fps when using only the TX2 CPU , with active power consumption under 10 W. FastDepth achieves close to state-of-the-art accuracy on the NYU Depth v2 dataset . To the best of the authors ’ knowledge , this paper demonstrates real-time monocular depth estimation using a deep neural network with the lowest latency and highest throughput on an embedded platform that can be carried by a micro aerial vehicle . Abstract Monocular depth estimation plays a crucial role in understanding 3D scene geometry and is a challenging computer vision task . Recently , deep convolutional neural networks have been applied to solve this problem . We propose a novel deep modular network architecture for indoor scene depth estimation from single RGB images . The proposed architecture consists of a main depth estimation network and two auxiliary semantic segmentation networks . This paper introduces the problem of long-range monocular depth estimation for outdoor urban environments . Range sensors and traditional depth estimation algorithms ( both stereo and single view ) predict depth for distances of less than 100 meters in outdoor settings and 10 meters in indoor settings . This work seeks to develop a more robust and an improved monocular vision based boundary avoidance for non-invasive stray control system for cattle , with a view to increase land mass coverage in virtual fencing techniques and dynamism . The monocular vision based depth estimation will be modeled using concept of global Fourier Transform ( FT ) and local Wavelet Transform ( WT ) of image structure of scenes ( boundaries ) . The magnitude of the global Fourier Transform gives the dominant orientations and textual patterns of the image ; while the local Wavelet Transform gives the dominant spectral features of the image and their spatial distribution . We carry out extensive analysis of this architecture including variants that operate on monocular RGB but use depth as side-information during training , unsupervised gating as a generic attentional mechanism , and multi-resolution gating . We find that gated pooling for joint semantic segmentation and depth yields state-of-the-art results for quantitative monocular depth estimation . Teaching a network to estimate depth via structure from motion allows it to generalize better to new environments with unfamiliar objects . This thesis extends recent work in unsupervised methods for single-view monocular depth estimation and uses the reconstruction losses for training posed in those works . Models and baseline models were evaluated on a variety of datasets and results indicate that indicate multiview models generalize across datasets better than previous work . paragraph : SUN - RGBD Most previous 3D detection works specialize either on outdoor LiDAR scans where objects are well separated in space and the point cloud is sparse ( so that it ’s feasible for bird ’s eye projection ) , or on indoor depth maps that are regular images with dense pixel values such that image CNNs can be easily applied . However , methods designed for bird ’s eye view may be incapable for indoor rooms where multiple objects often exist together in vertical space . To evaluate our method in a more realistic setting , we use scene understanding . Given an RGB image , we solve three tasks : semantic segmentation ( assigning pixel - level class labels ) , instance segmentation ( assigning pixel - level instance labels ) , and monocular depth estimation ( estimating continuous disparity per pixel ) . We follow the experimental procedure of [ reference ] and use an encoder - decoder architecture . However , there still exist two major issues with these DCNN-based depth map super-resolution methods that hinder the performance : 1 ) the low-resolution depth maps either need to be up-sampled before feeding into the network or substantial deconvolution has to be used and 2 ) the supervision ( high-resolution depth maps ) is only applied at the end of the network , thus it is difficult to handle large up-sampling factors , such as < inline-formula > < tex-math notation="LaTeX">$\times 8 $ < /tex-math></inline-formula > and < inline-formula > < tex-math notation="LaTeX">$\times 16 $ < /tex-math></inline-formula>. In this paper , we propose a new framework to tackle the above problems . First , we propose to represent the task of depth map super-resolution as a series of novel view synthesis sub-tasks . The novel view synthesis sub-task aims at generating ( synthesizing ) a depth map from a different camera pose , which could be learned in parallel . We propose the use of synthetic data for training and present a new dataset for optical flow evaluation and a tool for generating an unlimited quantity of ground truth correspondence data . We use this method for generating data to synthesize depth images for the problem of depth image super-resolution and show that it is superior to real data . We present results for optical flow confidence estimation with improved performance on a standard benchmark dataset . Depth image super-resolution reconstruction has gained significant popularity due to its practicability . However , conventional depth image super-resolution reconstruction methods access high frequency information either from a high-resolution depth image database or from a high-resolution color image of the same scene , which is limited in specific applications . This paper presents a Markov Random Field (MRF)-based approach for depth map super-resolution and enhancement . Given a low-resolution or moderate quality depth map , we study the problem of enhancing its resolution or quality with a registered high-resolution color image . Abstract . We propose a method for solving the depth map super-resolution problem . Given a low-resolution depth map as input , we recover a high-resolution depth map using a registered and potentially high-resolution color image . Experimental results show that proposed method improves both depth map coding efficiency and synthesized view quality . Additionally , the up-scaling method can be used for depth super-resolution reconstruction of depth data captured by depth sensors , like Kinect or TOF cameras . We present the bilateral solver , a novel algorithm for edge-aware smoothing that combines the flexibility and speed of simple filtering approaches with the accuracy of domain-specific optimization algorithms . Our technique is capable of matching or improving upon state-of-the-art results on several different computer vision tasks ( stereo , depth superresolution , colorization , and semantic segmentation ) while being 10–1000\(\times \ ) faster than baseline techniques with comparable accuracy , and producing lower-error output than techniques with comparable runtimes . The bilateral solver is fast , robust , straightforward to generalize to new domains , and simple to integrate into deep learning pipelines . This paper describes a method for high-quality depth superresolution . The standard formulations of image-guided depth upsampling , using simple joint filtering or quadratic optimization , lead to texture copying and depth bleeding artifacts . Very recently , FCNNs have become a popular choice for semantic segmentation , because of their effective feature generation and end - to - end training . FCNNs have also been applied to a range of other dense - prediction tasks recently , such as image restoration , image super - resolution and depth estimation . The method we propose here is similarly built upon fully convolution - style networks . Abstract Depth map upsampling will unavoidably smoothen the edges leading to blurry results on the depth boundaries , especially at large upscaling factors . Given that edges represent the most important cue in addressing the task of depth upsampling , we propose a novel depth upsampling framework based on deep edge-aware learning . Unlike existing CNN-based approaches that directly predict depth values from low resolution ( LR ) depth input , our framework firstly learns edge information of depth boundaries from the known LR depth map and its corresponding high resolution ( HR ) color image as reconstruction cues . Two novel approaches for depth map upsampling are presented . A depth upsampling method using image decomposition is first proposed . The primary goal of this work is to increase the depth resolution by a high-resolution RGB image to preserve sharp edges and details of the textures . Visual and quantitative experimental results show that the proposed method outperforms the JTF for depth map upsampling . We evaluate the upsampled and filtered low-resolution ( LR ) depth maps with their associated ground truth ( GT ) images . Compared with conventional filtering methods , our method provides clearer boundaries and performs especially better in recovering spatial variation from noisy images . We conduct a number of experiments for different applications : depth image upsampling , joint image denoising , details enhancement , and image abstraction . Both quantitative and qualitative comparisons demonstrate that our method outperforms state-of-the-art methods . In this paper we propose a sparsity-based , directional variational approach for upsampling depth images , aided by an accompanying optical ( in RGB ) image of higher spatial resolution . Compared to previously published works on RGB-D superresolution , the main innovations of this work are : 1 . performing depth image restoration in an overcomplete sparsity space derived from the directionalities of the RGB image ; 2 . refining the regularization term of the underlying inverse problem by a cross-validating spatial discontinuities in the optical and depth images . We compare our algorithm with the state of the art on the benchmark dataset and show that our approach provides more accurate depth upsampling with fewer artifacts . In addition , we show that the proposed algorithm is well suited for upsampling depth images using binary edge maps , an important sensor fusion application . The method is tested on the data coded using a mixed resolution 3D video codec implemented on top of the 3DV-ATM reference software and evaluated under the JCT-3V common test conditions . Simulation results show that our method can improve the depth-based super resolution upsampling by 0.64 % of dBR on average for total coded bitrate and 0.55 % of dBR on average for synthesized views . Aggregated coding gain with respect to full resolution scenario is improved by 7.71 % of dBR on average for total coded bitrate and 11.1 % dBR for synthesized views . The lost information in resolution reduction is recovered by an appropriate upsampling algorithm in decoding step . Most previous work considers the depth upsampling problem as common 2D map upsampling problem and do not take the intrinsic property of depth map into consideration . In this paper , we propose a novel two-step depth map upsampling scheme to address the problem on 3D videos . This paper describes a non-convex model that is carefully designed for high quality depth upsampling . Modern depth sensors such as time-of-flight cameras provide a promising depth measurement with video rate , but suffer from noise and low resolution . We investigated eight different promising recent image decomposition approaches under the same experimental setup . From our quantitative comparison , we can obtain novel and valuable insights into the image decomposition-based depth map upsampling : ( 1 ) the best image decomposition solution for depth map upsampling depends on the scaling factor of upsampling , ( 2 ) the guided filter-based image decomposition method gives rise to the best performance for lower scaling factors , whereas the tree filter-based image decomposition method leads to the best upsampling performance for higher scaling factors , and ( 3 ) the performance of image decomposition-based depth upsampling is not sensitive to image features . We believe that this comprehensive comparative study serves as a reference point and guide for developers and practitioners in choosing an appropriate image decomposition technique adopted for building depth map upsampling systems . Moreover , a joint adaptive kernel regression algorithm is designed to upsample the sparse depth map after the projection from Kinect v2 sensor 's depth camera to its full HD video camera . The structural information in the high resolution color image is implicitly utilized to guide the upsampling of depth map . The effectiveness of the proposed upsampling algorithm is illustrated by experimental results and comparisons on both real Kinect v2 data and Middlebury dataset .
Lidar has become an essential sensor for autonomous driving as it provides reliable depth estimation . Lidar is also the primary sensor used in building 3D maps which can be used even in the case of low-cost systems which do not use Lidar .
Each camera is calibrated separately and afterwards a stereo calibration is performed . Depth estimation is calculated using two different algorithms and an evaluation of their performance is presented . The mobile devices handle the computational load fast enough to be able to produce the results within the walk time at normal pace .
The most challenging issue in the field of visual control system is the accurate estimation of depth upon which all other parameters of pose estimation may depend . Here we have introduced a new approach for the depth estimation problem of a visual control system with a moving camera and stationary target . A unique visual pattern is designed to achieve the desired depth of the vision based system .
Although deep neural networks have been widely applied to computer vision problems , extending them into multiview depth estimation is non-trivial . In this paper , we present MVDepthNet , a convolutional network to solve the depth estimation problem given several image-pose pairs from a localized monocular camera in neighbor viewpoints . Multiview observations are encoded in a cost volume and then combined with the reference image to estimate the depth map using an encoder-decoder network .
However , the traditional maximum-likelihood based imaging method needs a long acquisition time to capture clear 3-D image at low light levels . To tackle this problem , we present a novel imaging method for depth estimate , which can obtain the accurate 3-D image in a short acquisition time . Our method combines the photon-count statistics with the temporal correlations of the reflected signal .
Intermediate supervision is used in several outputs of the proposed architecture in a staged approach . To aid the process of training and inference , hand segmentation masks are also estimated in such an intermediate supervision step , and used to guide the subsequent depth estimation process . In order to train and evaluate the proposed method we compile and make publicly available HandRGBD , a new dataset of 20,601 views of hands , each consisting of an RGB image and an aligned depth map .
After segmenting an image into planes from extracted line segments , data cost function and smoothness cost function for graph cut optimization is defined with the consideration of directions of line segments and neighborhood segments . Further , segments having same depth are grouped during depth estimation step using minimum spanning tree algorithm with proposed weights . Experimental result shows that the proposed method is not only able to identify complex Manhattan structures of indoor and outdoor scene but also provide exact boundaries and intersections of planes unlike earlier literature .
First , for the recovery of AIF video , the sparse prior of natural video is incorporated to ensure a high-quality defocus blur removal even under inaccurate depth estimation . Second , in depth estimation step , we feed back the spatial and temporal constraints from the high-quality AIF video and adopt a numerical solution , which is robust to the inaccuracy of AIF recovery to further boost the performance of depth from the defocus algorithm . Benefitting from the incorporation of AIF video priors and the temporal consistency constraint , the proposed framework can effectively reconstruct the depth of the textureless region and is insensitive to camera parameter changes .
For the problem of depth information estimation of micro vision image , a defocus characteristic parameter model based on Markov random field(MRF)is presented , which converts the depth information estimation problem of defocus characteristics into the optimization problem of energy function . With iterated conditional mode(ICM)algorithm applied to fulfill optimization and least squares estimate(LSE)algorithm employed to estimate the parameters of initial point , performance of the ICM algorithm is improved and the result of the algorithm is prevented from getting into local optimum .
Taking the advantage of motion vector which reflects the movement between neighboring frames , the depth map could be reconstructed . The motion vector calculated by traditional motion estimation algorithm is not appropriate for depth information estimation and the computational requirement is huge . So we introduce the parameter of mutual information ( MI ) .
2D-to-3D conversion is one of the key technologies in stereoscopic video . Its visual qualities depend on the qualities of depth image estimation and virtual viewpoint image rendering . Although there are many methods to get depth information , the depth information still has some errors because of calculation errors and noises , which influences the visual qualities of the generated virtual viewpoint image .
Inspired by the CS theory , this paper formulates the task of depth channel reconstruction as a sparsity-regularized least squares optimization problem . To solve this problem efficiently , an iterative algorithm based on the accelerated proximal gradient technique is developed , which not only speeds up the convergence rate , but also enhances the quality of depth image estimation . Several experiments are conducted and the results confirm the efficiency of the proposed approach .
In this paper , we propose a discriminative learning-based method for recovering the depth of a scene from multiple defocused images . The proposed method consists of a discriminative learning phase and a depth estimation phase . In the discriminative learning phase , we formalize depth from defocus ( DFD ) as a multi-class classification problem which can be solved by learning the discriminative metrics from the synthetic training set by minimizing a criterion function . To enhance the discriminative and generalization performance of the learned metrics , the criterion takes both within-class and between-class variations into account , and incorporates margin constraints . In the depth estimation phase , for each pixel , we compute the N discriminative functions and determine the depth level according to the minimum discriminant value . Experimental results on synthetic and real images show the effectiveness of our method in providing a reliable estimation of the depth of a scene .
For instance , matrix multiplication is used for feature covariance and covariance prediction computations . The computation takes more processing time when the number of features applied to the depth estimation calculation is increased . Therefore , there is a need to design a fast matrix multiplication to accelerate the processing speed .
In the first approach , the sharpness of objects in the refocused images is evaluated using the FFT method , from which an empirical formula can be obtained to estimate the depth of objects . The second method uses the raw data in sub-images to calculate an index , Average Absolute Difference ( AAD ) , for depth estimation purposes . The advantages and disadvantages of two proposed approaches are discussed and compared .
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 .
We further prove that optimizing this upper bound yields a Pareto optimal solution under realistic assumptions . We apply our method to a variety of multi - task deep learning problems including digit classification , scene understanding ( joint semantic segmentation , instance segmentation , and depth estimation ) , and multilabel classification . Our method produces higher - performing models than recent multi - task learning formulations or per - task training .
It is the champion of ImageNet scene parsing challenge 2016 , and arrived the 1st place on PASCAL VOC 2012 semantic segmentation benchmark , and the 1st place on urban scene Cityscapes data . They manifest that PSPNet gives a promising direction for pixel - level prediction tasks , which may even benefit CNN - based stereo matching , optical flow , depth estimation , etc . in follow - up work . Our main contributions are threefold .
While recent advances in convolutional neural networks have enabled accurate 2D detection in complex environments , the 3D object detection problem still remains an open challenge . Methods for 3D box regression from a single image , even including recent deep learning methods such as , still have relatively low accuracy especially in depth estimates at longer ranges . Hence , many current real - world systems either use stereo or augment their sensor stack with lidar and radar .
subsection : 3D Image Meshing The depth estimation of a face image can be conducted on the face region and external region respectively , with different requirements of accuracy . On the face region , we fit a 3DMM through the Multi - Features Framework ( MFF ) , see Fig .
Monocular depth estimation is a foundation task of three-dimensional ( 3D ) reconstruction which is used to improve the accuracy of environment perception . Because of the simpler hardware requirement , it is more suitable than other multi-view methods .
Deep Learning based depth mapping has provided appreciable results for shorter ranges , but this method remains unexplored for longer ranges . A Convolutional Neural Networks(CNN ) based approach fares equally well with the other methods of monocular depth estimation such as linear regression , support vector machine based regression , decision tree regression by treating the range estimation problem as a regression problem . A comparison has been done among various methods that have been used to approach the problem .
At present , deep learning has been applied more and more in monocular image depth estimation and has shown promising results . The current more ideal method for monocular depth estimation is the supervised learning based on ground truth depth , but this method requires an abundance of expensive ground truth depth as the supervised labels .
In the research of monocular image depth estimation , the fully convolutional residual network structure can achieve better prediction results , but the precision of image depth prediction still has room for improvement . Therefore , this paper proposes a feature of enhanced fully convolutional networks ( FE-FCN ) .
Recent models using deep neural networks have improved monocular depth estimation performance , but there is still difficulty in predicting absolute depth and generalizing outside a standard dataset . Here we introduce the paradigm of deep optics , i.e. end-to-end design of optics and image processing , to the monocular depth estimation problem , using coded defocus blur as an additional depth cue to be decoded by a neural network . We evaluate several optical coding strategies along with an end-to-end optimization scheme for depth estimation on three datasets , including NYU Depth v2 and KITTI .
This either leads to suboptimal results or the demand of huge amount of expensive ground truth labelled data to generate reasonable results . In this paper , we show for the first time that the monocular depth estimation problem can be reformulated as two sub-problems , a view synthesis procedure followed by stereo matching , with two intriguing properties , namely i ) geometrical constraints can be explicitly imposed during inference ; ii ) demand on labelled depth data can be greatly alleviated . We show that the whole pipeline can still be trained in an end-to-end fashion and this new formulation plays a critical role in advancing the performance .
Qualitatively , the estimated depth and renderings closely resemble the ground truth . ConclusionsThe proposed method shows a novel architecture to perform real-time monocular depth estimation without losing patient specificity in bronchoscopy . Future work will include integration within SLAM systems and collection of in vivo datasets .
Our proposed network , FastDepth , runs at 178 fps on an NVIDIA Jetson TX2 GPU and at 27 fps when using only the TX2 CPU , with active power consumption under 10 W. FastDepth achieves close to state-of-the-art accuracy on the NYU Depth v2 dataset . To the best of the authors ’ knowledge , this paper demonstrates real-time monocular depth estimation using a deep neural network with the lowest latency and highest throughput on an embedded platform that can be carried by a micro aerial vehicle .
Abstract Monocular depth estimation plays a crucial role in understanding 3D scene geometry and is a challenging computer vision task . Recently , deep convolutional neural networks have been applied to solve this problem .
We propose a novel deep modular network architecture for indoor scene depth estimation from single RGB images . The proposed architecture consists of a main depth estimation network and two auxiliary semantic segmentation networks .
This paper introduces the problem of long-range monocular depth estimation for outdoor urban environments . Range sensors and traditional depth estimation algorithms ( both stereo and single view ) predict depth for distances of less than 100 meters in outdoor settings and 10 meters in indoor settings .
This work seeks to develop a more robust and an improved monocular vision based boundary avoidance for non-invasive stray control system for cattle , with a view to increase land mass coverage in virtual fencing techniques and dynamism . The monocular vision based depth estimation will be modeled using concept of global Fourier Transform ( FT ) and local Wavelet Transform ( WT ) of image structure of scenes ( boundaries ) . The magnitude of the global Fourier Transform gives the dominant orientations and textual patterns of the image ; while the local Wavelet Transform gives the dominant spectral features of the image and their spatial distribution .
We carry out extensive analysis of this architecture including variants that operate on monocular RGB but use depth as side-information during training , unsupervised gating as a generic attentional mechanism , and multi-resolution gating . We find that gated pooling for joint semantic segmentation and depth yields state-of-the-art results for quantitative monocular depth estimation .
Teaching a network to estimate depth via structure from motion allows it to generalize better to new environments with unfamiliar objects . This thesis extends recent work in unsupervised methods for single-view monocular depth estimation and uses the reconstruction losses for training posed in those works . Models and baseline models were evaluated on a variety of datasets and results indicate that indicate multiview models generalize across datasets better than previous work .
paragraph : SUN - RGBD Most previous 3D detection works specialize either on outdoor LiDAR scans where objects are well separated in space and the point cloud is sparse ( so that it ’s feasible for bird ’s eye projection ) , or on indoor depth maps that are regular images with dense pixel values such that image CNNs can be easily applied . However , methods designed for bird ’s eye view may be incapable for indoor rooms where multiple objects often exist together in vertical space .
To evaluate our method in a more realistic setting , we use scene understanding . Given an RGB image , we solve three tasks : semantic segmentation ( assigning pixel - level class labels ) , instance segmentation ( assigning pixel - level instance labels ) , and monocular depth estimation ( estimating continuous disparity per pixel ) . We follow the experimental procedure of [ reference ] and use an encoder - decoder architecture .
However , there still exist two major issues with these DCNN-based depth map super-resolution methods that hinder the performance : 1 ) the low-resolution depth maps either need to be up-sampled before feeding into the network or substantial deconvolution has to be used and 2 ) the supervision ( high-resolution depth maps ) is only applied at the end of the network , thus it is difficult to handle large up-sampling factors , such as < inline-formula > < tex-math notation="LaTeX">$\times 8 $ < /tex-math></inline-formula > and < inline-formula > < tex-math notation="LaTeX">$\times 16 $ < /tex-math></inline-formula>. In this paper , we propose a new framework to tackle the above problems . First , we propose to represent the task of depth map super-resolution as a series of novel view synthesis sub-tasks . The novel view synthesis sub-task aims at generating ( synthesizing ) a depth map from a different camera pose , which could be learned in parallel .
We propose the use of synthetic data for training and present a new dataset for optical flow evaluation and a tool for generating an unlimited quantity of ground truth correspondence data . We use this method for generating data to synthesize depth images for the problem of depth image super-resolution and show that it is superior to real data . We present results for optical flow confidence estimation with improved performance on a standard benchmark dataset .
Depth image super-resolution reconstruction has gained significant popularity due to its practicability . However , conventional depth image super-resolution reconstruction methods access high frequency information either from a high-resolution depth image database or from a high-resolution color image of the same scene , which is limited in specific applications .
This paper presents a Markov Random Field (MRF)-based approach for depth map super-resolution and enhancement . Given a low-resolution or moderate quality depth map , we study the problem of enhancing its resolution or quality with a registered high-resolution color image .
Abstract . We propose a method for solving the depth map super-resolution problem . Given a low-resolution depth map as input , we recover a high-resolution depth map using a registered and potentially high-resolution color image .
Experimental results show that proposed method improves both depth map coding efficiency and synthesized view quality . Additionally , the up-scaling method can be used for depth super-resolution reconstruction of depth data captured by depth sensors , like Kinect or TOF cameras .
We present the bilateral solver , a novel algorithm for edge-aware smoothing that combines the flexibility and speed of simple filtering approaches with the accuracy of domain-specific optimization algorithms . Our technique is capable of matching or improving upon state-of-the-art results on several different computer vision tasks ( stereo , depth superresolution , colorization , and semantic segmentation ) while being 10–1000\(\times \ ) faster than baseline techniques with comparable accuracy , and producing lower-error output than techniques with comparable runtimes . The bilateral solver is fast , robust , straightforward to generalize to new domains , and simple to integrate into deep learning pipelines .
This paper describes a method for high-quality depth superresolution . The standard formulations of image-guided depth upsampling , using simple joint filtering or quadratic optimization , lead to texture copying and depth bleeding artifacts .
Very recently , FCNNs have become a popular choice for semantic segmentation , because of their effective feature generation and end - to - end training . FCNNs have also been applied to a range of other dense - prediction tasks recently , such as image restoration , image super - resolution and depth estimation . The method we propose here is similarly built upon fully convolution - style networks .
Abstract Depth map upsampling will unavoidably smoothen the edges leading to blurry results on the depth boundaries , especially at large upscaling factors . Given that edges represent the most important cue in addressing the task of depth upsampling , we propose a novel depth upsampling framework based on deep edge-aware learning . Unlike existing CNN-based approaches that directly predict depth values from low resolution ( LR ) depth input , our framework firstly learns edge information of depth boundaries from the known LR depth map and its corresponding high resolution ( HR ) color image as reconstruction cues .
Two novel approaches for depth map upsampling are presented . A depth upsampling method using image decomposition is first proposed .
The primary goal of this work is to increase the depth resolution by a high-resolution RGB image to preserve sharp edges and details of the textures . Visual and quantitative experimental results show that the proposed method outperforms the JTF for depth map upsampling . We evaluate the upsampled and filtered low-resolution ( LR ) depth maps with their associated ground truth ( GT ) images .
Compared with conventional filtering methods , our method provides clearer boundaries and performs especially better in recovering spatial variation from noisy images . We conduct a number of experiments for different applications : depth image upsampling , joint image denoising , details enhancement , and image abstraction . Both quantitative and qualitative comparisons demonstrate that our method outperforms state-of-the-art methods .
In this paper we propose a sparsity-based , directional variational approach for upsampling depth images , aided by an accompanying optical ( in RGB ) image of higher spatial resolution . Compared to previously published works on RGB-D superresolution , the main innovations of this work are : 1 . performing depth image restoration in an overcomplete sparsity space derived from the directionalities of the RGB image ; 2 . refining the regularization term of the underlying inverse problem by a cross-validating spatial discontinuities in the optical and depth images .
We compare our algorithm with the state of the art on the benchmark dataset and show that our approach provides more accurate depth upsampling with fewer artifacts . In addition , we show that the proposed algorithm is well suited for upsampling depth images using binary edge maps , an important sensor fusion application .
The method is tested on the data coded using a mixed resolution 3D video codec implemented on top of the 3DV-ATM reference software and evaluated under the JCT-3V common test conditions . Simulation results show that our method can improve the depth-based super resolution upsampling by 0.64 % of dBR on average for total coded bitrate and 0.55 % of dBR on average for synthesized views . Aggregated coding gain with respect to full resolution scenario is improved by 7.71 % of dBR on average for total coded bitrate and 11.1 % dBR for synthesized views .
The lost information in resolution reduction is recovered by an appropriate upsampling algorithm in decoding step . Most previous work considers the depth upsampling problem as common 2D map upsampling problem and do not take the intrinsic property of depth map into consideration . In this paper , we propose a novel two-step depth map upsampling scheme to address the problem on 3D videos .
This paper describes a non-convex model that is carefully designed for high quality depth upsampling . Modern depth sensors such as time-of-flight cameras provide a promising depth measurement with video rate , but suffer from noise and low resolution .
We investigated eight different promising recent image decomposition approaches under the same experimental setup . From our quantitative comparison , we can obtain novel and valuable insights into the image decomposition-based depth map upsampling : ( 1 ) the best image decomposition solution for depth map upsampling depends on the scaling factor of upsampling , ( 2 ) the guided filter-based image decomposition method gives rise to the best performance for lower scaling factors , whereas the tree filter-based image decomposition method leads to the best upsampling performance for higher scaling factors , and ( 3 ) the performance of image decomposition-based depth upsampling is not sensitive to image features . We believe that this comprehensive comparative study serves as a reference point and guide for developers and practitioners in choosing an appropriate image decomposition technique adopted for building depth map upsampling systems .
Moreover , a joint adaptive kernel regression algorithm is designed to upsample the sparse depth map after the projection from Kinect v2 sensor 's depth camera to its full HD video camera . The structural information in the high resolution color image is implicitly utilized to guide the upsampling of depth map . The effectiveness of the proposed upsampling algorithm is illustrated by experimental results and comparisons on both real Kinect v2 data and Middlebury dataset .
Real-time Dynamic Object Detection for Autonomous Driving using Prior 3D-Maps Depth estimation for visually impaired people using mobile devices A new technique for geometry based visual depth estimation for uncalibrated camera MVDepthNet: Real-Time Multiview Depth Estimation Neural Network Adaptive Depth Imaging With Single-Photon Detectors Learning to Infer the Depth Map of a Hand from its Color Image Line-based single view 3D reconstruction in Manhattan world for augmented reality Extracting Depth and Radiance From a Defocused Video Pair Application of MRF to Depth Information Estimation of Micro Image Defocus An adaptive motion estimation algorithm based on mutual information for depth information estimation A depth optimization method for 2D-to-3D conversion based on RGB-D images Efficient Depth Image Reconstruction using Accelerated Proximal Gradient Method Depth from Defocus via Discriminative Metric Learning Improving Monocular SLAM Inverse Depth Parameterization Computation Time Via Software Profiling and Parallel Matrix Multiplication Depth estimation of light field data from pinhole-masked DSLR cameras Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression Multi-Task Learning as Multi-Objective Optimization Pyramid Scene Parsing Network PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation Face Alignment Across Large Poses: A 3D Solution Monocular Depth Estimation Based on Multi-Scale Graph Convolution Networks Deep Learning Based Approach for Range Estimation Unsupervised monocular stereo matching Feature Enhanced Fully Convolutional Networks for Monocular Depth Estimation Deep Optics for Monocular Depth Estimation and 3D Object Detection Single View Stereo Matching Deep monocular 3D reconstruction for assisted navigation in bronchoscopy FastDepth: Fast Monocular Depth Estimation on Embedded Systems A contextual conditional random field network for monocular depth estimation Deep Modular Network Architecture for Depth Estimation from Single Indoor Images FarSight: Long-Range Depth Estimation from Outdoor Images Monocular Vision Based Boundary Avoidance for Non-Invasive Stray Control System for Cattle: A Conceptual Approach Recurrent Scene Parsing with Perspective Understanding in the Loop Multiview monocular depth estimation using unsupervised learning methods Frustum PointNets for 3D Object Detection from RGB-D Data Multi-Task Learning as Multi-Objective Optimization Deeply Supervised Depth Map Super-Resolution as Novel View Synthesis Supervised algorithm selection for flow and other computer vision problems Joint nonlocal sparse representation for depth map super-resolution A revisit to MRF-based depth map super-resolution and enhancement Depth map super-resolution via local and nonlocal priors Edge-Guided Depth Map Resampling for HEVC 3D Video Coding The Fast Bilateral Solver Structure Selective Depth Superresolution for RGB-D Cameras Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation Depth upsampling based on deep edge-aware learning Depth map upsampling with image decomposition Improved depth map upsampling using iterative joint trilateral filter Edge-Aware Filtering with Local Polynomial Approximation and Rectangle-Based Weighting Sparsity-based depth image restoration using surface priors and RGB-D correlations Joint Geodesic Upsampling of Depth Images View synthesis quality mapping for depth-based super resolution on mixed resolution 3D video A novel upsampling scheme for depth map compression in 3DTV system A majorize-minimize approach for high-quality depth upsampling Objective Evaluation of Image Decomposition Algorithms for Depth Map Upsampling Depth map restoration and upsampling for kinect v2 based on IR-depth consistency and joint adaptive kernel regression
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2018 2014 2009 2018 2016 2018 2015 2015 2008 2010 2014 2018 2011 2011 2010 2017 2018 2017 2018 2016 2020 2018 2018 2019 2019 2018 2017 2019 2020 2018 2018 2015 2017 2018 2018 2018 2019 2014 2017 2011 2014 2013 2016 2016 2016 2020 2015 2018 2016 2015 2013 2014 2010 2015 2019 2015
ECCV Workshops (5) LASCAS VRCAI KSE ICONIP (3) ICIP 2017 IEEE International Conference on Computer Vision (ICCV) NeurIPS 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) DSAA ECCV Workshops (1) IROS 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition NeurIPS ICIP ICASSP ECCV (3) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICIP CVPR 3DTV-Conference PCS ICIP ISCAS
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