AU2017324923B2 - Predicting depth from image data using a statistical model - Google Patents

Predicting depth from image data using a statistical model Download PDF

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AU2017324923B2
AU2017324923B2 AU2017324923A AU2017324923A AU2017324923B2 AU 2017324923 B2 AU2017324923 B2 AU 2017324923B2 AU 2017324923 A AU2017324923 A AU 2017324923A AU 2017324923 A AU2017324923 A AU 2017324923A AU 2017324923 B2 AU2017324923 B2 AU 2017324923B2
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image
disparity values
predicted
model
disparity
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Gabriel Brostow
Clement GODARD
Oisin Mac Aodha
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Niantic Spatial Inc
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    • G06T11/00—2D [Two Dimensional] image generation
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    • H—ELECTRICITY
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    • H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
    • H04N2013/0074—Stereoscopic image analysis
    • H04N2013/0081—Depth or disparity estimation from stereoscopic image signals

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AU2017324923A 2016-09-12 2017-09-12 Predicting depth from image data using a statistical model Active AU2017324923B2 (en)

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GB1615470.0A GB2553782B (en) 2016-09-12 2016-09-12 Predicting depth from image data using a statistical model
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PCT/GB2017/052671 WO2018046964A1 (en) 2016-09-12 2017-09-12 Predicting depth from image data using a statistical model

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Families Citing this family (126)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3330664B1 (en) * 2015-07-29 2021-01-27 KYOCERA Corporation Parallax calculating device, stereo camera device, vehicle, and parallax calculating method
US10834406B2 (en) 2016-12-12 2020-11-10 Netflix, Inc. Device-consistent techniques for predicting absolute perceptual video quality
US10803546B2 (en) * 2017-11-03 2020-10-13 Baidu Usa Llc Systems and methods for unsupervised learning of geometry from images using depth-normal consistency
CN109785376B (zh) * 2017-11-15 2023-02-28 富士通株式会社 深度估计装置的训练方法、深度估计设备及存储介质
US10643383B2 (en) * 2017-11-27 2020-05-05 Fotonation Limited Systems and methods for 3D facial modeling
US11042163B2 (en) 2018-01-07 2021-06-22 Nvidia Corporation Guiding vehicles through vehicle maneuvers using machine learning models
US10740876B1 (en) * 2018-01-23 2020-08-11 Facebook Technologies, Llc Systems and methods for generating defocus blur effects
CN108335322B (zh) * 2018-02-01 2021-02-12 深圳市商汤科技有限公司 深度估计方法和装置、电子设备、程序和介质
WO2019152888A1 (en) 2018-02-02 2019-08-08 Nvidia Corporation Safety procedure analysis for obstacle avoidance in autonomous vehicle
CN111133447B (zh) 2018-02-18 2024-03-19 辉达公司 适于自主驾驶的对象检测和检测置信度的方法和系统
DE112019000122T5 (de) 2018-02-27 2020-06-25 Nvidia Corporation Echtzeiterfassung von spuren und begrenzungen durch autonome fahrzeuge
WO2019178548A1 (en) 2018-03-15 2019-09-19 Nvidia Corporation Determining drivable free-space for autonomous vehicles
US11080590B2 (en) 2018-03-21 2021-08-03 Nvidia Corporation Stereo depth estimation using deep neural networks
WO2019191306A1 (en) 2018-03-27 2019-10-03 Nvidia Corporation Training, testing, and verifying autonomous machines using simulated environments
CN108734693B (zh) * 2018-03-30 2019-10-25 百度在线网络技术（北京）有限公司 用于生成信息的方法和装置
CN108537837B (zh) * 2018-04-04 2023-05-05 腾讯科技（深圳）有限公司 一种深度信息确定的方法及相关装置
WO2019202487A1 (en) * 2018-04-17 2019-10-24 Eth Zurich Robotic camera software and controller
US11082681B2 (en) 2018-05-17 2021-08-03 Niantic, Inc. Self-supervised training of a depth estimation system
CN108961327B (zh) * 2018-05-22 2021-03-30 深圳市商汤科技有限公司 一种单目深度估计方法及其装置、设备和存储介质
SG11201811261SA (en) * 2018-06-14 2020-01-30 Beijing Didi Infinity Technology & Development Co Ltd Systems and methods for updating a high-resolution map based on binocular images
US11966838B2 (en) 2018-06-19 2024-04-23 Nvidia Corporation Behavior-guided path planning in autonomous machine applications
DE102019113114A1 (de) 2018-06-19 2019-12-19 Nvidia Corporation Verhaltensgesteuerte wegplanung in autonomen maschinenanwendungen
TW202006738A (zh) * 2018-07-12 2020-02-01 國立臺灣科技大學 應用機器學習的醫學影像分析方法及其系統
CN109166144B (zh) * 2018-07-20 2021-08-24 中国海洋大学 一种基于生成对抗网络的图像深度估计方法
CN109213147A (zh) * 2018-08-01 2019-01-15 上海交通大学 一种基于深度学习的机器人避障轨迹规划方法及系统
RU2698402C1 (ru) * 2018-08-30 2019-08-26 Самсунг Электроникс Ко., Лтд. Способ обучения сверточной нейронной сети для восстановления изображения и система для формирования карты глубины изображения (варианты)
US10986325B2 (en) * 2018-09-12 2021-04-20 Nvidia Corporation Scene flow estimation using shared features
CN113168541B (zh) * 2018-10-15 2024-07-09 泰立戴恩菲力尔商业系统公司 用于成像系统的深度学习推理系统和方法
US11507822B2 (en) * 2018-10-31 2022-11-22 General Electric Company Scalable artificial intelligence model generation systems and methods for healthcare
JP6946255B2 (ja) * 2018-11-13 2021-10-06 株式会社東芝 学習装置、推定装置、学習方法およびプログラム
US11610115B2 (en) 2018-11-16 2023-03-21 Nvidia Corporation Learning to generate synthetic datasets for training neural networks
CN109712228B (zh) * 2018-11-19 2023-02-24 中国科学院深圳先进技术研究院 建立三维重建模型的方法、装置、电子设备及存储介质
US11170299B2 (en) 2018-12-28 2021-11-09 Nvidia Corporation Distance estimation to objects and free-space boundaries in autonomous machine applications
WO2020140049A1 (en) 2018-12-28 2020-07-02 Nvidia Corporation Distance to obstacle detection in autonomous machine applications
DE112019006468T5 (de) 2018-12-28 2021-10-14 Nvidia Corporation Erkennung des abstands zu hindernissen bei anwendungen mit autonomen maschinen
CN111383256B (zh) * 2018-12-29 2024-05-17 北京市商汤科技开发有限公司 图像处理方法、电子设备及计算机可读存储介质
DE102019100303A1 (de) 2019-01-08 2020-07-09 HELLA GmbH & Co. KGaA Verfahren und Vorrichtung zum Ermitteln einer Krümmung einer Fahrbahn
US11520345B2 (en) 2019-02-05 2022-12-06 Nvidia Corporation Path perception diversity and redundancy in autonomous machine applications
US10956755B2 (en) * 2019-02-19 2021-03-23 Tesla, Inc. Estimating object properties using visual image data
CN111583321A (zh) * 2019-02-19 2020-08-25 富士通株式会社 图像处理装置、方法及介质
US10839543B2 (en) * 2019-02-26 2020-11-17 Baidu Usa Llc Systems and methods for depth estimation using convolutional spatial propagation networks
US11648945B2 (en) 2019-03-11 2023-05-16 Nvidia Corporation Intersection detection and classification in autonomous machine applications
WO2020181465A1 (en) * 2019-03-11 2020-09-17 Moqi Technology (beijing) Co., Ltd. Device and method for contactless fingerprint acquisition
CN109919993B (zh) * 2019-03-12 2023-11-07 腾讯科技（深圳）有限公司 视差图获取方法、装置和设备及控制系统
CN113906271B (zh) 2019-04-12 2025-05-09 辉达公司 用于自主机器应用的使用地图信息增强的地面实况数据的神经网络训练
CN113808061B (zh) * 2019-04-28 2024-11-22 深圳市商汤科技有限公司 一种图像处理方法及装置
US11044462B2 (en) 2019-05-02 2021-06-22 Niantic, Inc. Self-supervised training of a depth estimation model using depth hints
CN109996056B (zh) * 2019-05-08 2021-03-26 北京奇艺世纪科技有限公司 一种2d视频转3d视频的方法、装置及电子设备
CN110113595B (zh) * 2019-05-08 2021-04-30 北京奇艺世纪科技有限公司 一种2d视频转3d视频的方法、装置及电子设备
CN110111244B (zh) * 2019-05-08 2024-01-26 北京奇艺世纪科技有限公司 图像转换、深度图预测和模型训练方法、装置及电子设备
CN110490919B (zh) * 2019-07-05 2023-04-18 天津大学 一种基于深度神经网络的单目视觉的深度估计方法
US11138751B2 (en) * 2019-07-06 2021-10-05 Toyota Research Institute, Inc. Systems and methods for semi-supervised training using reprojected distance loss
CN110443843A (zh) * 2019-07-29 2019-11-12 东北大学 一种基于生成对抗网络的无监督单目深度估计方法
CN110415284B (zh) * 2019-07-31 2022-04-19 中国科学技术大学 一种单视彩色图像深度图获得方法及装置
US11468585B2 (en) * 2019-08-27 2022-10-11 Nec Corporation Pseudo RGB-D for self-improving monocular slam and depth prediction
CN110610486B (zh) * 2019-08-28 2022-07-19 清华大学 单目图像深度估计方法及装置
US11698272B2 (en) 2019-08-31 2023-07-11 Nvidia Corporation Map creation and localization for autonomous driving applications
EP4025395A1 (en) 2019-09-07 2022-07-13 Embodied Intelligence, Inc. Training artificial networks for robotic picking
US12059813B2 (en) * 2019-09-07 2024-08-13 Embodied Intelligence, Inc. Determine depth with pixel-to-pixel image correspondence for three-dimensional computer vision
EP4025394A1 (en) 2019-09-07 2022-07-13 Embodied Intelligence, Inc. Systems and methods for robotic picking and perturbation
CN114641378B (zh) 2019-09-07 2024-09-27 具现智能有限公司 用于机器人拣选的系统和方法
CN110738697B (zh) * 2019-10-10 2023-04-07 福州大学 基于深度学习的单目深度估计方法
CN111047630B (zh) * 2019-11-13 2023-06-13 芯启源(上海)半导体科技有限公司 神经网络和基于神经网络的目标检测及深度预测方法
CN111047634B (zh) * 2019-11-13 2023-08-08 杭州飞步科技有限公司 场景深度的确定方法、装置、设备及存储介质
US11157774B2 (en) * 2019-11-14 2021-10-26 Zoox, Inc. Depth data model training with upsampling, losses, and loss balancing
JP7645257B2 (ja) * 2019-11-14 2025-03-13 ズークス インコーポレイテッド アップサンプリング、損失、および損失均衡による深度データモデルトレーニング
WO2021111482A1 (en) * 2019-12-02 2021-06-10 Alma Mater Studiorum – Università Di Bologna Method to determine the depth from images by self-adaptive learning of a neural network and system thereof
CN111192238B (zh) * 2019-12-17 2022-09-20 南京理工大学 基于自监督深度网络的无损血管三维测量方法
CN111027508B (zh) * 2019-12-23 2022-09-06 电子科技大学 一种基于深层神经网络的遥感图像覆被变化检测方法
US11288522B2 (en) 2019-12-31 2022-03-29 Woven Planet North America, Inc. Generating training data from overhead view images
US11037328B1 (en) * 2019-12-31 2021-06-15 Lyft, Inc. Overhead view image generation
US11244500B2 (en) 2019-12-31 2022-02-08 Woven Planet North America, Inc. Map feature extraction using overhead view images
CN111242999B (zh) * 2020-01-10 2022-09-20 大连理工大学 基于上采样及精确重匹配的视差估计优化方法
CN111310916B (zh) * 2020-01-22 2022-10-25 浙江省北大信息技术高等研究院 一种区分左右眼图片的深度系统训练方法及系统
US12112468B2 (en) 2020-01-30 2024-10-08 Electronics And Telecommunications Research Institute Method and apparatus for detecting dimension error
CN115666406A (zh) * 2020-02-06 2023-01-31 维卡瑞斯外科手术公司 在外科手术机器人系统中确定体内深度感知的系统和方法
CN111314686B (zh) * 2020-03-20 2021-06-25 深圳市博盛医疗科技有限公司 一种自动优化3d立体感的方法、系统及介质
CN111523409B (zh) 2020-04-09 2023-08-29 北京百度网讯科技有限公司 用于生成位置信息的方法和装置
US12456046B2 (en) * 2020-04-20 2025-10-28 Nvidia Corporation Distance determinations using one or more neural networks
US12077190B2 (en) 2020-05-18 2024-09-03 Nvidia Corporation Efficient safety aware path selection and planning for autonomous machine applications
CN113724311B (zh) * 2020-05-25 2024-04-02 北京四维图新科技股份有限公司 深度图获取方法、设备及存储介质
US12080013B2 (en) * 2020-07-06 2024-09-03 Toyota Research Institute, Inc. Multi-view depth estimation leveraging offline structure-from-motion
KR102809044B1 (ko) * 2020-07-29 2025-05-19 삼성전자주식회사 영상의 깊이를 추정하는 방법 및 장치
US12008740B2 (en) * 2020-08-12 2024-06-11 Niantic, Inc. Feature matching using features extracted from perspective corrected image
JP7389729B2 (ja) * 2020-09-10 2023-11-30 株式会社日立製作所 障害物検知装置、障害物検知システム及び障害物検知方法
US11747468B2 (en) 2020-09-24 2023-09-05 Eagle Technology, Llc System using a priori terrain height data for interferometric synthetic aperture radar (IFSAR) phase disambiguation and related methods
US11238307B1 (en) 2020-09-24 2022-02-01 Eagle Technology, Llc System for performing change detection within a 3D geospatial model based upon semantic change detection using deep learning and related methods
US11587249B2 (en) 2020-09-24 2023-02-21 Eagle Technology, Llc Artificial intelligence (AI) system and methods for generating estimated height maps from electro-optic imagery
US11302071B1 (en) 2020-09-24 2022-04-12 Eagle Technology, Llc Artificial intelligence (AI) system using height seed initialization for extraction of digital elevation models (DEMs) and related methods
CN112330795B (zh) * 2020-10-10 2022-10-28 清华大学 基于单张rgbd图像的人体三维重建方法及系统
US11978266B2 (en) 2020-10-21 2024-05-07 Nvidia Corporation Occupant attentiveness and cognitive load monitoring for autonomous and semi-autonomous driving applications
DE102020006971A1 (de) * 2020-11-13 2022-05-19 Alexander Bayer Kamerabasiertes Assistenzsystem mit Künstlicher Intelligenz für blinde Personen
TWI784349B (zh) * 2020-11-16 2022-11-21 國立政治大學 顯著圖產生方法及使用該方法的影像處理系統
CN112465888A (zh) * 2020-11-16 2021-03-09 电子科技大学 一种基于单目视觉的无监督深度估计方法
TWI837557B (zh) * 2020-12-12 2024-04-01 美商尼安蒂克公司 用於自監督多圖框單眼深度估計模型之電腦實施方法及非暫時性電腦可讀儲存媒體
CN112330675B (zh) * 2020-12-15 2022-08-23 南昌工程学院 基于AOD-Net的交通道路图像大气能见度检测方法
CN112802079A (zh) * 2021-01-19 2021-05-14 奥比中光科技集团股份有限公司 一种视差图获取方法、装置、终端和存储介质
KR102319237B1 (ko) * 2021-03-02 2021-10-29 인하대학교 산학협력단 핸드크래프트 비용 기반의 다중 뷰 스테레오 정합 방법
TWI790560B (zh) 2021-03-03 2023-01-21 宏碁股份有限公司 並排影像偵測方法與使用該方法的電子裝置
US11908100B2 (en) * 2021-03-15 2024-02-20 Qualcomm Incorporated Transform matrix learning for multi-sensor image capture devices
JP7447042B2 (ja) 2021-03-17 2024-03-11 株式会社東芝 画像処理装置、方法及びプログラム
JP7557425B2 (ja) * 2021-05-06 2024-09-27 富士フイルム株式会社 学習装置、深度情報取得装置、内視鏡システム、学習方法、及びプログラム
AU2022282850A1 (en) 2021-05-25 2024-01-18 Niantic Spatial, Inc. Image depth prediction with wavelet decomposition
KR102489890B1 (ko) * 2021-05-28 2023-01-17 한국항공대학교산학협력단 깊이 추정 시스템 및 깊이 추정 방법
US12159466B2 (en) * 2021-06-07 2024-12-03 Autobrains Technologies Ltd Context based lane prediction
KR102717662B1 (ko) * 2021-07-02 2024-10-15 주식회사 뷰웍스 스테레오 영상을 이용한 고심도 영상 생성 방법 및 장치, 고심도 영상 생성 모델 학습 장치
US11961249B2 (en) * 2021-07-14 2024-04-16 Black Sesame Technologies Inc. Generating stereo-based dense depth images
CN113762278B (zh) * 2021-09-13 2023-11-17 中冶路桥建设有限公司 一种基于目标检测的沥青路面损坏识别方法
US20240412563A1 (en) * 2021-10-20 2024-12-12 Airy3D Inc. Methods and systems using depth imaging for training and deploying neural networks for biometric anti-spoofing
CN114401391B (zh) * 2021-12-09 2023-01-06 北京邮电大学 虚拟视点生成方法及装置
CN114494386B (zh) * 2021-12-14 2025-05-27 南京大学 一种多频谱图像监督的红外图像深度估计方法
KR20230106453A (ko) 2022-01-06 2023-07-13 삼성전자주식회사 전자 장치 및 전자 장치의 동작 방법
KR102559936B1 (ko) * 2022-01-28 2023-07-27 포티투닷 주식회사 단안 카메라를 이용하여 깊이 정보를 추정하는 방법 및 장치
US12190535B2 (en) * 2022-03-07 2025-01-07 Black Sesame Technologies Inc. Generating depth images for image data
KR102531286B1 (ko) * 2022-03-29 2023-05-12 포티투닷 주식회사 깊이 정보 추정 모델 학습을 위한 데이터 처리 방법 및 장치
CN117274347A (zh) * 2022-06-09 2023-12-22 鸿海精密工业股份有限公司 自编码器训练方法、系统及深度影像生成方法
CN114782911B (zh) * 2022-06-20 2022-09-16 小米汽车科技有限公司 图像处理的方法、装置、设备、介质、芯片及车辆
CN115205147B (zh) * 2022-07-13 2025-06-06 福州大学 一种基于Transformer的多尺度优化低照度图像增强方法
KR102813053B1 (ko) * 2022-07-14 2025-05-28 재단법인대구경북과학기술원 단안 카메라 이미지에 대한 깊이 추정 방법
US12288278B2 (en) 2022-10-04 2025-04-29 The Weather Company, Llc Layering modifications defined in a rule on a set of objects detected within a frame
CN116258756B (zh) * 2023-02-23 2024-03-08 齐鲁工业大学(山东省科学院) 一种自监督单目深度估计方法及系统
FR3151116B1 (fr) * 2023-07-10 2025-09-26 Psa Automobiles Sa Procédé et dispositif de détermination d’une profondeur d’un objet par un système de vision auto-supervisé.
CN117388850B (zh) * 2023-10-13 2024-10-29 广东省科学院广州地理研究所 一种海面风场微波遥感反演数据的空间降尺度方法
FR3160789A1 (fr) * 2024-03-26 2025-10-03 Stellantis Auto Sas Procédé et dispositif d’apprentissage d’un modèle de prédiction de profondeur associé à un système de vision stéréoscopique par comparaison de positions de points dans une scène tridimensionnelle.
WO2025219748A1 (en) * 2024-04-19 2025-10-23 Abu Dhabi Gas Development Company Limited Sand accumulation elevation system and a method for elevating sand accumulation
CN119810169B (zh) * 2024-12-16 2025-06-03 云南民族大学 无人机视角下大景深场景的单目深度估计方法

Family Cites Families (27)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5577130A (en) * 1991-08-05 1996-11-19 Philips Electronics North America Method and apparatus for determining the distance between an image and an object
JP2005165614A (ja) * 2003-12-02 2005-06-23 Canon Inc 画像合成装置および画像合成方法
CN101689299B (zh) * 2007-06-20 2016-04-13 汤姆逊许可证公司 用于图像的立体匹配的系统和方法
JP5153940B2 (ja) * 2008-06-24 2013-02-27 トムソン ライセンシング 動き補償を用いた画像の奥行き抽出のためのシステムおよび方法
CN101605270B (zh) * 2009-07-16 2011-02-16 清华大学 生成深度图的方法和装置
GB2473282B (en) * 2009-09-08 2011-10-12 Nds Ltd Recommended depth value
US9030530B2 (en) * 2009-12-15 2015-05-12 Thomson Licensing Stereo-image quality and disparity/depth indications
CN101840574B (zh) * 2010-04-16 2012-05-23 西安电子科技大学 基于边缘象素特征的深度估计方法
US20110304618A1 (en) * 2010-06-14 2011-12-15 Qualcomm Incorporated Calculating disparity for three-dimensional images
TR201010438A2 (tr) * 2010-12-14 2012-07-23 Vestel Elektroni̇k Sanayi̇ Ve Ti̇caret A.Ş. Stereo video için enformasyon geçirgenliği temelinde disparite tahmini.
CN102523464A (zh) * 2011-12-12 2012-06-27 上海大学 一种双目立体视频的深度图像估计方法
US9117295B2 (en) * 2011-12-20 2015-08-25 Adobe Systems Incorporated Refinement of depth maps by fusion of multiple estimates
US9571810B2 (en) * 2011-12-23 2017-02-14 Mediatek Inc. Method and apparatus of determining perspective model for depth map generation by utilizing region-based analysis and/or temporal smoothing
CN103106651B (zh) * 2012-07-16 2015-06-24 清华大学深圳研究生院 一种基于三维hough变换的获取视差平面的方法
CN102831601A (zh) * 2012-07-26 2012-12-19 中北大学 基于联合相似性测度和自适应支持权重的立体匹配方法
AU2013305770A1 (en) * 2012-08-21 2015-02-26 Pelican Imaging Corporation Systems and methods for parallax detection and correction in images captured using array cameras
NL2009616C2 (en) * 2012-10-11 2014-04-14 Ultra D Co Peratief U A Adjusting depth in a three-dimensional image signal.
CN103295229B (zh) * 2013-05-13 2016-01-20 清华大学深圳研究生院 视频深度信息恢复的全局立体匹配方法
US9317925B2 (en) * 2013-07-22 2016-04-19 Stmicroelectronics S.R.L. Depth map generation method, related system and computer program product
EP2887312A1 (en) * 2013-12-18 2015-06-24 Nokia Corporation Method, apparatus and computer program product for depth estimation of stereo images
EP2887311B1 (en) * 2013-12-20 2016-09-14 Thomson Licensing Method and apparatus for performing depth estimation
CN103955954B (zh) * 2014-04-21 2017-02-08 杭州电子科技大学 一种结合同场景立体图对的高分辨率深度图像重建方法
EP2950269A1 (en) * 2014-05-27 2015-12-02 Thomson Licensing Method and apparatus for improving estimation of disparity in a stereo image pair using a hybrid recursive matching processing
CN104065947B (zh) * 2014-06-18 2016-06-01 长春理工大学 一种集成成像系统的深度图获取方法
CN104408710B (zh) * 2014-10-30 2017-05-24 北京大学深圳研究生院 一种全局视差估计方法和系统
KR20160056132A (ko) * 2014-11-11 2016-05-19 삼성전자주식회사 영상 변환 장치 및 그 영상 변환 방법
US10200666B2 (en) * 2015-03-04 2019-02-05 Dolby Laboratories Licensing Corporation Coherent motion estimation for stereoscopic video

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
R Garg et al:"Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue" The University of Adelaide 16 March 2016 *
W Zhu et al:"Variational Stereo Matching with Left Right Consistency Constraint" Soft Computing and Pattern Recognition, IEEE, 14 October 2011 *

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