KR102487270B1 - 통계적 모델을 이용한 이미지 데이터로부터의 깊이 예측 - Google Patents

통계적 모델을 이용한 이미지 데이터로부터의 깊이 예측 Download PDF

Info

Publication number
KR102487270B1
KR102487270B1 KR1020197010331A KR20197010331A KR102487270B1 KR 102487270 B1 KR102487270 B1 KR 102487270B1 KR 1020197010331 A KR1020197010331 A KR 1020197010331A KR 20197010331 A KR20197010331 A KR 20197010331A KR 102487270 B1 KR102487270 B1 KR 102487270B1
Authority
KR
South Korea
Prior art keywords
image
predicted
disparity
model
disparity value
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
KR1020197010331A
Other languages
English (en)
Korean (ko)
Other versions
KR20190065287A (ko
Inventor
클레멘트 고다드
오이신 맥 아오다
가브리엘 브로스토브
Original Assignee
나이앤틱, 인크.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 나이앤틱, 인크. filed Critical 나이앤틱, 인크.
Publication of KR20190065287A publication Critical patent/KR20190065287A/ko
Application granted granted Critical
Publication of KR102487270B1 publication Critical patent/KR102487270B1/ko
Assigned to 나이앤틱, 스파셜 인크. reassignment 나이앤틱, 스파셜 인크. 권리의 전부이전등록 Assignors: 나이앤틱, 인크.
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/08Learning methods
    • G06N3/084Backpropagation, e.g. using gradient descent
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/50Depth or shape recovery
    • G06T7/55Depth or shape recovery from multiple images
    • G06T7/593Depth or shape recovery from multiple images from stereo images
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • G06F17/16Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00Pattern recognition
    • G06F18/20Analysing
    • G06F18/24Classification techniques
    • G06F18/241Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2413Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
    • G06F18/24133Distances to prototypes
    • G06F18/24143Distances to neighbourhood prototypes, e.g. restricted Coulomb energy networks [RCEN]
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/04Architecture, e.g. interconnection topology
    • G06N3/045Combinations of networks
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/04Architecture, e.g. interconnection topology
    • G06N3/045Combinations of networks
    • G06N3/0455Auto-encoder networks; Encoder-decoder networks
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/04Architecture, e.g. interconnection topology
    • G06N3/0464Convolutional networks [CNN, ConvNet]
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/04Architecture, e.g. interconnection topology
    • G06N3/047Probabilistic or stochastic networks
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/08Learning methods
    • G06N3/0895Weakly supervised learning, e.g. semi-supervised or self-supervised learning
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T1/00General purpose image data processing
    • G06T1/20Processor architectures; Processor configuration, e.g. pipelining
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00Two-dimensional [2D] image generation
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00Geometric image transformations in the plane of the image
    • G06T3/40Scaling of whole images or parts thereof, e.g. expanding or contracting
    • G06T3/4007Scaling of whole images or parts thereof, e.g. expanding or contracting based on interpolation, e.g. bilinear interpolation
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/50Depth or shape recovery
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00Arrangements for image or video recognition or understanding
    • G06V10/70Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00Arrangements for image or video recognition or understanding
    • G06V10/70Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10004Still image; Photographic image
    • G06T2207/10012Stereo images
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10024Color image
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20076Probabilistic image processing
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20081Training; Learning
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20084Artificial neural networks [ANN]
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04NPICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N13/00Stereoscopic video systems; Multi-view video systems; Details thereof
    • H04N2013/0074Stereoscopic image analysis
    • H04N2013/0081Depth or disparity estimation from stereoscopic image signals

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Computing Systems (AREA)
  • Software Systems (AREA)
  • Artificial Intelligence (AREA)
  • Data Mining & Analysis (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Mathematical Physics (AREA)
  • General Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biomedical Technology (AREA)
  • Molecular Biology (AREA)
  • Computational Linguistics (AREA)
  • Biophysics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Databases & Information Systems (AREA)
  • Medical Informatics (AREA)
  • Multimedia (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Algebra (AREA)
  • Probability & Statistics with Applications (AREA)
  • Evolutionary Biology (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Image Analysis (AREA)
  • Image Processing (AREA)
KR1020197010331A 2016-09-12 2017-09-12 통계적 모델을 이용한 이미지 데이터로부터의 깊이 예측 Active KR102487270B1 (ko)

Applications Claiming Priority (3)

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

Publications (2)

Publication Number Publication Date
KR20190065287A KR20190065287A (ko) 2019-06-11
KR102487270B1 true KR102487270B1 (ko) 2023-01-11

Family

ID=57234660

Family Applications (1)

Application Number Title Priority Date Filing Date
KR1020197010331A Active KR102487270B1 (ko) 2016-09-12 2017-09-12 통계적 모델을 이용한 이미지 데이터로부터의 깊이 예측

Country Status (9)

Country Link
US (1) US11100401B2 (https=)
EP (1) EP3510561B1 (https=)
JP (1) JP7177062B2 (https=)
KR (1) KR102487270B1 (https=)
CN (1) CN109791697B (https=)
AU (1) AU2017324923B2 (https=)
CA (1) CA3035298C (https=)
GB (1) GB2553782B (https=)
WO (1) WO2018046964A1 (https=)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20220198694A1 (en) * 2020-01-10 2022-06-23 Dalian University Of Technology Disparity estimation optimization method based on upsampling and exact rematching
KR20240009825A (ko) * 2022-07-14 2024-01-23 재단법인대구경북과학기술원 단안 카메라 이미지에 대한 깊이 추정 방법

Families Citing this family (129)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10614585B2 (en) * 2015-07-29 2020-04-07 Kyocera Corporation Parallax calculation apparatus, stereo camera apparatus, vehicle, and parallax calculation 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
DE112019000065B4 (de) 2018-02-02 2025-01-09 Nvidia Corporation Sicherheitsprozeduranalyse zur hindernisvermeidung in einem autonomen fahrzeug
DE112019000049T5 (de) 2018-02-18 2020-01-23 Nvidia Corporation Für autonomes fahren geeignete objekterfassung und erfassungssicherheit
US10997433B2 (en) 2018-02-27 2021-05-04 Nvidia Corporation Real-time detection of lanes and boundaries by autonomous vehicles
CN110494863B (zh) 2018-03-15 2024-02-09 辉达公司 确定自主车辆的可驾驶自由空间
WO2019182974A2 (en) 2018-03-21 2019-09-26 Nvidia Corporation Stereo depth estimation using deep neural networks
DE112019001605T5 (de) 2018-03-27 2020-12-17 Nvidia Corporation Trainieren, testen und verifizieren von autonomen maschinen unter verwendung simulierter umgebungen
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
JP7397001B2 (ja) * 2018-04-17 2023-12-12 シアヴィジョン・ゲゼルシャフト・ミト・ベシュレンクテル・ハフツング ロボットカメラソフトウェアと制御装置
KR102506959B1 (ko) * 2018-05-17 2023-03-07 나이앤틱, 인크. 깊이 추정 시스템의 자가 감독 훈련
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
WO2020081470A1 (en) * 2018-10-15 2020-04-23 Flir Commercial Systems, Inc. Deep learning inference systems and methods for imaging systems
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
CN113039563B (zh) 2018-11-16 2024-03-12 辉达公司 学习生成用于训练神经网络的合成数据集
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
US11182916B2 (en) 2018-12-28 2021-11-23 Nvidia Corporation Distance to obstacle detection in autonomous machine applications
WO2020140047A1 (en) 2018-12-28 2020-07-02 Nvidia Corporation Distance to obstacle detection in autonomous machine applications
US11170299B2 (en) 2018-12-28 2021-11-09 Nvidia Corporation Distance estimation to objects and free-space boundaries in autonomous machine applications
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
WO2020185779A1 (en) 2019-03-11 2020-09-17 Nvidia Corporation Intersection detection and classification in autonomous machine applications
KR20210123385A (ko) * 2019-03-11 2021-10-13 엠오큐아이 테크놀로지 (베이징) 씨오., 엘티디. 비접촉 지문 획득 장치 및 방법
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
DE112020001897T5 (de) 2019-04-12 2021-12-30 Nvidia Corporation Trainieren neuronaler Netze unter Verwendung von Grundwahrheitsdaten, die mit Karteninformationen ergänzt wurden, für autonome Maschinenanwendungen
CN113808062B (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
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
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
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
US11788861B2 (en) 2019-08-31 2023-10-17 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
WO2021046528A1 (en) 2019-09-07 2021-03-11 Embodied Intelligence, Inc. Systems and methods for robotic picking
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
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
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
CN114981834A (zh) * 2019-11-14 2022-08-30 祖克斯有限公司 使用上采样、损失与损失平衡进行深度数据模型训练
US11157774B2 (en) * 2019-11-14 2021-10-26 Zoox, Inc. Depth data model training with upsampling, losses, and loss balancing
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
US11244500B2 (en) 2019-12-31 2022-02-08 Woven Planet North America, Inc. Map feature extraction using overhead view images
US11037328B1 (en) 2019-12-31 2021-06-15 Lyft, Inc. Overhead view image generation
US11288522B2 (en) 2019-12-31 2022-03-29 Woven Planet North America, Inc. Generating training data from overhead view images
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
EP4099914A4 (en) * 2020-02-06 2024-02-28 Vicarious Surgical Inc. SYSTEM AND METHOD FOR DETERMINING DEPTH PERCEPTION IN VIVO IN A SURGICAL ROBOT SYSTEM
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
US12073579B2 (en) * 2020-06-09 2024-08-27 Interdigital Ce Patent Holdings, Sas Local light field flow as an alternative to plane-sweep volumes
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
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
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
US12080010B2 (en) * 2020-12-12 2024-09-03 Niantic, Inc. Self-supervised multi-frame monocular depth estimation model
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
AU2022282850B2 (en) 2021-05-25 2026-03-19 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
EP4420043A4 (en) * 2021-10-20 2025-07-23 Airy3D Inc METHODS AND SYSTEMS USING DEPTH IMAGING FOR TRAINING AND DEPLOYING NEURAL NETWORKS FOR BIOMETRIC ANTI-SPOOKING
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
CN117152223B (zh) * 2022-05-24 2025-12-12 鸿海精密工业股份有限公司 深度图像生成方法、系统、电子设备及可读存储介质
CN117274347B (zh) * 2022-06-09 2026-04-10 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
CN115222917A (zh) * 2022-07-19 2022-10-21 腾讯科技(深圳)有限公司 三维重建模型的训练方法、装置、设备及存储介质
KR102893120B1 (ko) * 2022-08-11 2025-12-03 한국전자기술연구원 좁은 베이스라인을 갖는 스테레오 카메라를 위한 고해상 깊이 추정 방법 및 시스템
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
FR3160789B1 (fr) * 2024-03-26 2026-02-13 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
US20260037461A1 (en) * 2024-07-31 2026-02-05 Nvidia Corporation Systems and methods for processing data based at least on random regions in a frame
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

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2011525657A (ja) 2008-06-24 2011-09-22 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

Family Cites Families (26)

* 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
WO2008156450A1 (en) * 2007-06-20 2008-12-24 Thomson Licensing System and method for stereo matching of images
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
WO2011081646A1 (en) * 2009-12-15 2011-07-07 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
CN107346061B (zh) * 2012-08-21 2020-04-24 快图有限公司 用于使用阵列照相机捕捉的图像中的视差检测和校正的系统和方法
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

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2011525657A (ja) 2008-06-24 2011-09-22 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

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Ravi Garg 등, Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue, arXiv:1603.04992v1.(2016.05.16.)*
Wenqiao Zhu 등, Variational Stereo Matching with Left Right Consistency Constraint, 2011 SoCPaR.(2011.10.14.)*

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20220198694A1 (en) * 2020-01-10 2022-06-23 Dalian University Of Technology Disparity estimation optimization method based on upsampling and exact rematching
US12008779B2 (en) * 2020-01-10 2024-06-11 Dalian University Of Technology Disparity estimation optimization method based on upsampling and exact rematching
KR20240009825A (ko) * 2022-07-14 2024-01-23 재단법인대구경북과학기술원 단안 카메라 이미지에 대한 깊이 추정 방법
KR102813053B1 (ko) 2022-07-14 2025-05-28 재단법인대구경북과학기술원 단안 카메라 이미지에 대한 깊이 추정 방법

Also Published As

Publication number Publication date
CA3035298A1 (en) 2018-03-15
GB2553782B (en) 2021-10-20
JP7177062B2 (ja) 2022-11-22
WO2018046964A1 (en) 2018-03-15
JP2019526878A (ja) 2019-09-19
CN109791697B (zh) 2023-10-13
EP3510561B1 (en) 2022-03-02
CA3035298C (en) 2023-03-21
GB201615470D0 (en) 2016-10-26
GB2553782A (en) 2018-03-21
AU2017324923A1 (en) 2019-04-11
AU2017324923B2 (en) 2022-01-27
BR112019004798A2 (pt) 2019-06-04
BR112019004798A8 (pt) 2023-04-04
EP3510561A1 (en) 2019-07-17
US20190213481A1 (en) 2019-07-11
CN109791697A (zh) 2019-05-21
US11100401B2 (en) 2021-08-24
KR20190065287A (ko) 2019-06-11

Similar Documents

Publication Publication Date Title
KR102487270B1 (ko) 통계적 모델을 이용한 이미지 데이터로부터의 깊이 예측
US12260575B2 (en) Scale-aware monocular localization and mapping
Poggi et al. Towards real-time unsupervised monocular depth estimation on cpu
CN112991413A (zh) 自监督深度估测方法和系统
EP3819869B1 (en) Method and apparatus with depth image generation
EP3769265A1 (en) Localisation, mapping and network training
JP2022519194A (ja) 奥行き推定
CN112396645A (zh) 一种基于卷积残差学习的单目图像深度估计方法和系统
JP7789798B2 (ja) 顔表情、身体ポーズ形状及び衣服パフォーマンスキャプチャのための暗黙的微分可能レンダラーを用いたマルチビューニューラル人間予測
US12450755B2 (en) Systems and methods for motion estimation and view prediction
CN117333627A (zh) 一种自动驾驶场景的重建与补全方法、系统及存储介质
CN120580680A (zh) 跨模态融合三维目标检测方法、系统及存储介质
JP2025038761A (ja) 画像処理装置、画像処理方法、及びプログラム
Harisankar et al. Unsupervised depth estimation from monocular images for autonomous vehicles
CN120655693A (zh) 基于可信区域深度可控生成的单目自监督深度估计方法
Hosseinzadeh et al. Unsupervised learning of camera pose with compositional re-estimation
JP2023047846A (ja) 距離推定装置、距離推定方法、および距離推定用コンピュータプログラム
Lu et al. Self-supervised depth estimation from spectral consistency and novel view synthesis
Xu et al. TKO‐SLAM: Visual SLAM algorithm based on time‐delay feature regression and keyframe pose optimization
WO2024183896A1 (en) Neural radiance fields for hand-object interaction modelling using a contact optimization field
US20250196863A1 (en) Generating a road-height profile
JP2025038752A (ja) 画像処理装置、画像処理方法、及びプログラム
Zhang et al. A Review of Vision-Based Depth Estimation: Current Methods and Future Directions
BR112019004798B1 (pt) Método implantado por computador e mídia de armazenamento
CN120107742A (zh) 一种模型训练方法、设备及介质

Legal Events

Date Code Title Description
PA0105 International application

St.27 status event code: A-0-1-A10-A15-nap-PA0105

P11-X000 Amendment of application requested

St.27 status event code: A-2-2-P10-P11-nap-X000

P13-X000 Application amended

St.27 status event code: A-2-2-P10-P13-nap-X000

R15-X000 Change to inventor requested

St.27 status event code: A-3-3-R10-R15-oth-X000

R16-X000 Change to inventor recorded

St.27 status event code: A-3-3-R10-R16-oth-X000

R18-X000 Changes to party contact information recorded

St.27 status event code: A-3-3-R10-R18-oth-X000

P11-X000 Amendment of application requested

St.27 status event code: A-2-2-P10-P11-nap-X000

P13-X000 Application amended

St.27 status event code: A-2-2-P10-P13-nap-X000

PG1501 Laying open of application

St.27 status event code: A-1-1-Q10-Q12-nap-PG1501

A201 Request for examination
P11-X000 Amendment of application requested

St.27 status event code: A-2-2-P10-P11-nap-X000

P13-X000 Application amended

St.27 status event code: A-2-2-P10-P13-nap-X000

PA0201 Request for examination

St.27 status event code: A-1-2-D10-D11-exm-PA0201

E902 Notification of reason for refusal
PE0902 Notice of grounds for rejection

St.27 status event code: A-1-2-D10-D21-exm-PE0902

T11-X000 Administrative time limit extension requested

St.27 status event code: U-3-3-T10-T11-oth-X000

P11-X000 Amendment of application requested

St.27 status event code: A-2-2-P10-P11-nap-X000

P13-X000 Application amended

St.27 status event code: A-2-2-P10-P13-nap-X000

E701 Decision to grant or registration of patent right
PE0701 Decision of registration

St.27 status event code: A-1-2-D10-D22-exm-PE0701

P22-X000 Classification modified

St.27 status event code: A-2-2-P10-P22-nap-X000

PR0701 Registration of establishment

St.27 status event code: A-2-4-F10-F11-exm-PR0701

PR1002 Payment of registration fee

St.27 status event code: A-2-2-U10-U12-oth-PR1002

Fee payment year number: 1

PG1601 Publication of registration

St.27 status event code: A-4-4-Q10-Q13-nap-PG1601

P22-X000 Classification modified

St.27 status event code: A-4-4-P10-P22-nap-X000

PN2301 Change of applicant

St.27 status event code: A-5-5-R10-R11-asn-PN2301

R11 Change to the name of applicant or owner or transfer of ownership requested

Free format text: ST27 STATUS EVENT CODE: A-5-5-R10-R11-ASN-PN2301 (AS PROVIDED BY THE NATIONAL OFFICE)

PN2301 Change of applicant

St.27 status event code: A-5-5-R10-R14-asn-PN2301

R14 Transfer of ownership recorded

Free format text: ST27 STATUS EVENT CODE: A-5-5-R10-R14-ASN-PN2301 (AS PROVIDED BY THE NATIONAL OFFICE)