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

Gu et al., 2020 - Google Patents

OnionNet: Single-view depth prediction and camera pose estimation for unlabeled video

Gu et al., 2020

View PDF
Document ID
16025546698509098081
Author
Gu T
Wang Z
Li D
Yang H
Du W
Zhou Y
Publication year
Publication venue
IEEE Transactions on Cognitive and Developmental Systems

External Links

Snippet

In real scenes, humans can easily infer their positions and distances from other objects with their own eyes. To make the robots have the same visual ability, this article presents an unsupervised OnionNet framework, including LeafNet and ParachuteNet, for single-view …
Continue reading at www.researchgate.net (PDF) (other versions)

Classifications

    • 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
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20112Image segmentation details
    • 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
    • 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
    • G06K9/6201Matching; Proximity measures
    • G06K9/6202Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10016Video; Image sequence
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • 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
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • 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
    • G06K9/00624Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • 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
    • 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
    • G06T11/002D [Two Dimensional] image generation
    • 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
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T13/00Animation

Similar Documents

Publication Publication Date Title
Wang et al. Mvster: Epipolar transformer for efficient multi-view stereo
Wei et al. Deepsfm: Structure from motion via deep bundle adjustment
Shu et al. Feature-metric loss for self-supervised learning of depth and egomotion
Xu et al. Aanet: Adaptive aggregation network for efficient stereo matching
Liu et al. Local similarity pattern and cost self-reassembling for deep stereo matching networks
Yin et al. Scale recovery for monocular visual odometry using depth estimated with deep convolutional neural fields
Tong et al. Adaptive cost volume representation for unsupervised high-resolution stereo matching
Joung et al. Unsupervised stereo matching using confidential correspondence consistency
He et al. Learning scene dynamics from point cloud sequences
Duan et al. RGB-Fusion: Monocular 3D reconstruction with learned depth prediction
Lin et al. Unsupervised monocular visual odometry with decoupled camera pose estimation
Lin et al. Efficient and high-quality monocular depth estimation via gated multi-scale network
Ren et al. DeepSFM: robust deep iterative refinement for structure from motion
Gu et al. OnionNet: Single-view depth prediction and camera pose estimation for unlabeled video
Liu et al. Mono-ViFI: A Unified Learning Framework for Self-supervised Single and Multi-frame Monocular Depth Estimation
Ou et al. A scene segmentation algorithm combining the body and the edge of the object
Yusiong et al. AsiANet: Autoencoders in autoencoder for unsupervised monocular depth estimation
Liu et al. Robust visual odometry using sparse optical flow network
Dai et al. Unsupervised learning of depth estimation based on attention model and global pose optimization
Cao et al. IBCO-Net: Integrity-boundary-corner optimization in a general multistage network for building fine segmentation from remote sensing images
Zhou et al. DecoupledPoseNet: Cascade decoupled pose learning for unsupervised camera ego-motion estimation
Xia et al. PCDR-DFF: Multi-modal 3D object detection based on point cloud diversity representation and dual feature fusion
Habekost et al. Learning 3D Global Human Motion Estimation from Unpaired, Disjoint Datasets.
Wei et al. LAM-depth: Laplace-attention module-based self-supervised monocular depth estimation
Xu et al. 4d contrastive superflows are dense 3d representation learners