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

Yang et al., 2021 - Google Patents

Monocular depth estimation based on multi-scale depth map fusion

Yang et al., 2021

View PDF
Document ID
5517054249183354289
Author
Yang X
Chang Q
Liu X
He S
Cui Y
Publication year
Publication venue
IEEE Access

External Links

Snippet

Monocular depth estimation is a basic task in machine vision. In recent years, the performance of monocular depth estimation has been greatly improved. However, most depth estimation networks are based on a very deep network to extract features that lead to …
Continue reading at ieeexplore.ieee.org (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; 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; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20112Image segmentation details
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30781Information retrieval; Database structures therefor; File system structures therefor of video data
    • G06F17/30784Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre
    • G06F17/30799Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre using low-level visual features of the video content
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00Three dimensional [3D] modelling, e.g. data description of 3D objects
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T9/00Image coding, e.g. from bit-mapped to non bit-mapped
    • G06T9/001Model-based coding, e.g. wire frame
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2200/00Indexing scheme for image data processing or generation, in general
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F3/00Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements

Similar Documents

Publication Publication Date Title
Zhou et al. LSNet: Lightweight spatial boosting network for detecting salient objects in RGB-thermal images
CN112001960B (en) Monocular image depth estimation method based on multi-scale residual error pyramid attention network model
CN111047548B (en) Attitude transformation data processing method and device, computer equipment and storage medium
Tang et al. HRTransNet: HRFormer-driven two-modality salient object detection
Chen et al. Joint hand-object 3d reconstruction from a single image with cross-branch feature fusion
Bansal et al. Marr revisited: 2d-3d alignment via surface normal prediction
Chen et al. Modality-induced transfer-fusion network for RGB-D and RGB-T salient object detection
Zhang et al. Deep hierarchical guidance and regularization learning for end-to-end depth estimation
Tu et al. Consistent 3d hand reconstruction in video via self-supervised learning
Li et al. Learning face image super-resolution through facial semantic attribute transformation and self-attentive structure enhancement
Xue et al. Boundary-induced and scene-aggregated network for monocular depth prediction
Yang et al. Monocular depth estimation based on multi-scale depth map fusion
Xu et al. THCANet: Two-layer hop cascaded asymptotic network for robot-driving road-scene semantic segmentation in RGB-D images
Feng et al. U²-Former: Nested U-Shaped Transformer for Image Restoration via Multi-View Contrastive Learning
Zeng et al. Dual swin-transformer based mutual interactive network for RGB-D salient object detection
Pan et al. Multi-stage feature pyramid stereo network-based disparity estimation approach for two to three-dimensional video conversion
Zhang et al. Spatial-information guided adaptive context-aware network for efficient RGB-D semantic segmentation
CN116129289A (en) Attention edge interaction optical remote sensing image saliency target detection method
Zhou et al. Boundary-guided lightweight semantic segmentation with multi-scale semantic context
Tang et al. Sparse2dense: From direct sparse odometry to dense 3-d reconstruction
Cong et al. Multi-Projection Fusion and Refinement Network for Salient Object Detection in 360$^{\circ} $ Omnidirectional Image
Chen et al. Hybrid attention fusion embedded in transformer for remote sensing image semantic segmentation
Lin et al. Efficient and high-quality monocular depth estimation via gated multi-scale network
KR20200073967A (en) Method and apparatus for determining target object in image based on interactive input
Yang et al. Monocular camera based real-time dense mapping using generative adversarial network