[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Duan et al., 2021 - Google Patents

RGB-Fusion: Monocular 3D reconstruction with learned depth prediction

Duan et al., 2021

Document ID
12537580107783572891
Author
Duan Z
Chen Y
Yu H
Hu B
Chen C
Publication year
Publication venue
Displays

External Links

Snippet

Generating large-scale and high-quality 3D scene reconstruction from monocular images is an essential technical foundation in augmented reality and robotics. However, the apparent shortcomings (eg, scale ambiguity, dense depth estimation in texture-less areas) make …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20112—Image segmentation details
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2200/00—Indexing scheme for image data processing or generation, in general
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T15/00—3D [Three Dimensional] image rendering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/01—Social networking

Similar Documents

Publication Publication Date Title
Hu et al. Deep depth completion from extremely sparse data: A survey
Zhou et al. Unsupervised learning of depth and ego-motion from video
Chang et al. RC-MVSNet: Unsupervised multi-view stereo with neural rendering
Qi et al. Geonet++: Iterative geometric neural network with edge-aware refinement for joint depth and surface normal estimation
Bozic et al. Neural non-rigid tracking
Duan et al. RGB-Fusion: Monocular 3D reconstruction with learned depth prediction
Yang et al. Depth map super-resolution using stereo-vision-assisted model
Lin et al. Unsupervised monocular visual odometry with decoupled camera pose estimation
Liu et al. Learning optical flow and scene flow with bidirectional camera-lidar fusion
Li et al. Omnidirectional stereo depth estimation based on spherical deep network
Yue et al. Self-supervised monocular depth estimation in dynamic scenes with moving instance loss
Xiu et al. Robust self-supervised monocular visual odometry based on prediction-update pose estimation network
Zhang et al. Linearly augmented real-time 4D expressional face capture
Fang et al. Self-supervised learning of depth and ego-motion from videos by alternative training and geometric constraints from 3-d to 2-d
Wang et al. Splatflow: Learning multi-frame optical flow via splatting
Li et al. Depth estimation based on monocular camera sensors in autonomous vehicles: A self-supervised learning approach
Božič et al. Neural non-rigid tracking
Shen et al. ImLiDAR: cross-sensor dynamic message propagation network for 3D object detection
Zhang et al. Edge aware depth inference for large-scale aerial building multi-view stereo
Zhou et al. Miper-MVS: Multi-scale iterative probability estimation with refinement for efficient multi-view stereo
Qi et al. Sparse prior guided deep multi-view stereo
Wang et al. An attention-based and deep sparse priori cascade multi-view stereo network for 3D reconstruction
Li et al. DRI-MVSNet: A depth residual inference network for multi-view stereo images
Liang et al. Planar object tracking benchmark in the wild
Liu et al. Joint estimation of pose, depth, and optical flow with a competition–cooperation transformer network