JP7177062B2 - 統計モデルを用いた画像データからの深度予測 - Google Patents

統計モデルを用いた画像データからの深度予測 Download PDF

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JP7177062B2
JP7177062B2 JP2019535986A JP2019535986A JP7177062B2 JP 7177062 B2 JP7177062 B2 JP 7177062B2 JP 2019535986 A JP2019535986 A JP 2019535986A JP 2019535986 A JP2019535986 A JP 2019535986A JP 7177062 B2 JP7177062 B2 JP 7177062B2
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predicted
disparity
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disparity value
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クレメント ゴダール
エイダ オシン マック
ガブリエル ブロストウ
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/50—Depth or shape recovery
    • G06T7/55—Depth or shape recovery from multiple images
    • G06T7/593—Depth or shape recovery from multiple images from stereo images
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
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    • G06N3/0464—Convolutional networks [CNN, ConvNet]
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    • G06N3/0895—Weakly supervised learning, e.g. semi-supervised or self-supervised learning
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    • G06T1/00—General purpose image data processing
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
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    • G06T11/00—Two-dimensional [2D] image generation
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
    • G06T3/4007—Scaling of whole images or parts thereof, e.g. expanding or contracting based on interpolation, e.g. bilinear interpolation
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    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • G—PHYSICS
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10004—Still image; Photographic image
    • G06T2207/10012—Stereo images
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    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color image
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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    • G06T2207/20—Special algorithmic details
    • G06T2207/20076—Probabilistic image processing
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    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
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    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • 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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JP2019535986A 2016-09-12 2017-09-12 統計モデルを用いた画像データからの深度予測 Active JP7177062B2 (ja)

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

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CN (1) CN109791697B (enExample)
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Families Citing this family (132)

* 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
US10798387B2 (en) 2016-12-12 2020-10-06 Netflix, Inc. Source-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
DE112019000070T5 (de) 2018-01-07 2020-03-12 Nvidia Corporation Führen von fahrzeugen durch fahrzeugmanöver unter verwendung von modellen für maschinelles lernen
US10664953B1 (en) * 2018-01-23 2020-05-26 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
US11079764B2 (en) 2018-02-02 2021-08-03 Nvidia Corporation Safety procedure analysis for obstacle avoidance in autonomous vehicles
WO2019161300A1 (en) 2018-02-18 2019-08-22 Nvidia Corporation Detecting objects and determining confidence scores
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
EP3781366A1 (en) * 2018-04-17 2021-02-24 ETH Zürich Robotic camera software and controller
WO2019222467A1 (en) * 2018-05-17 2019-11-21 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
AU2018278901B2 (en) * 2018-06-14 2020-07-16 Beijing Didi Infinity Technology And 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
WO2020102733A1 (en) 2018-11-16 2020-05-22 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
WO2020140049A1 (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
DE112019006484T5 (de) 2018-12-28 2021-10-21 Nvidia Corporation Detektion von abständen zu hindernissen in autonomen maschinenanwendungen
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
WO2020163390A1 (en) 2019-02-05 2020-08-13 Nvidia Corporation Driving lane perception diversity and redundancy in autonomous driving applications
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
US10956755B2 (en) * 2019-02-19 2021-03-23 Tesla, Inc. Estimating object properties using visual image data
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
US12399015B2 (en) 2019-04-12 2025-08-26 Nvidia Corporation Neural network training using ground truth data augmented with map information for autonomous machine applications
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
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
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
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
US11713978B2 (en) 2019-08-31 2023-08-01 Nvidia Corporation Map creation and localization for autonomous driving applications
WO2021046530A1 (en) * 2019-09-07 2021-03-11 Embodied Intelligence, Inc. Three-dimensional computer vision system for robotic devices
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
US11911901B2 (en) 2019-09-07 2024-02-27 Embodied Intelligence, Inc. Training artificial networks 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
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
EP4058974A4 (en) * 2019-11-14 2023-12-13 Zoox, Inc. Depth data model training with upsampling, losses, and loss balancing
US11157774B2 (en) * 2019-11-14 2021-10-26 Zoox, Inc. Depth data model training with upsampling, losses, and loss balancing
CN114761998A (zh) * 2019-12-02 2022-07-15 大学之母博洛尼亚大学 通过神经网络的自适应学习从图像确定深度的方法及其系统
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
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
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
JP7794450B2 (ja) * 2020-02-06 2026-01-06 ヴィカリアス・サージカル・インコーポレイテッド 外科用ロボットシステムにおける生体内の深さ知覚を決定するためのシステムおよび方法
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
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
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
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
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
US12663290B2 (en) 2021-04-21 2026-06-23 Nvidia Corporation Map health monitoring for autonomous systems and applications
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
WO2023065038A1 (en) 2021-10-20 2023-04-27 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
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
KR102813053B1 (ko) * 2022-07-14 2025-05-28 재단법인대구경북과학기술원 단안 카메라 이미지에 대한 깊이 추정 방법
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
US12659442B1 (en) * 2025-04-04 2026-06-16 Qualcomm Incorporated Stereo depth adaptation with viewpoint virtualization

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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

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
CA2687213C (en) * 2007-06-20 2015-12-22 Thomson Licensing System and method for stereo matching of images
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
EP3869797B1 (en) * 2012-08-21 2023-07-19 Adeia Imaging LLC Method for depth detection 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
US9373171B2 (en) * 2013-07-22 2016-06-21 Stmicroelectronics S.R.L. Method for generating a depth map, 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
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

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
Ravi Garg, Vijay Kumar B G, Gustavo Carneiro, Ian Reid，Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue，arxiv.org，米国，CORNELL UNIVERSITY，2016年03月16日，https://arxiv.org/pdf/1603.04992v1

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