[go: up one dir, main page]

Guan et al., 2022 - Google Patents

Multistage pixel-visibility learning with cost regularization for multiview stereo

Guan et al., 2022

Document ID
12280216547077683421
Author
Guan X
Tong W
Jiang S
Sun P
Wu E
Chen G
Publication year
Publication venue
IEEE Transactions on Automation Science and Engineering

External Links

Snippet

Multiple-view stereo has potential applications in robotic operations and autonomous driving (unstructured environment construction, visual servo). With assisted depth information, inertial navigation systems can achieve precise navigation. It is, especially suitable for GPS …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • 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
    • G06K9/4604—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections
    • G06K9/4609—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections by matching or filtering
    • 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
    • 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
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • 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
    • 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
    • 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
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6201—Matching; Proximity measures
    • G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • 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
    • G06T15/00—3D [Three Dimensional] image rendering
    • 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
    • G06T13/00—Animation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
    • G06T3/0068—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image for image registration, e.g. elastic snapping
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T1/00—General purpose image data processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models

Similar Documents

Publication Publication Date Title
Chen et al. Point-based multi-view stereo network
Luo et al. Learning optical flow with adaptive graph reasoning
JP7178396B2 (en) Method and computer system for generating data for estimating 3D pose of object included in input image
Zhou et al. Improved itracker combined with bidirectional long short-term memory for 3D gaze estimation using appearance cues
Wang et al. Hierarchical attention learning of scene flow in 3d point clouds
Tu et al. Consistent 3d hand reconstruction in video via self-supervised learning
Tian et al. Unsupervised learning of optical flow with CNN-based non-local filtering
CN108416840A (en) A Dense Reconstruction Method of 3D Scene Based on Monocular Camera
CN1509456A (en) Method and system using data-driven model for monocular face tracking
Tong et al. Normal assisted pixel-visibility learning with cost aggregation for multiview stereo
Zhai et al. Optical flow estimation using channel attention mechanism and dilated convolutional neural networks
Zhang et al. Spatial information-guided adaptive context-aware network for efficient rgb-d semantic segmentation
Guan et al. Multistage pixel-visibility learning with cost regularization for multiview stereo
Hwang et al. Lidar depth completion using color-embedded information via knowledge distillation
Che et al. SDOF-GAN: Symmetric dense optical flow estimation with generative adversarial networks
Deng et al. Detail preserving coarse-to-fine matching for stereo matching and optical flow
Luo et al. FD-SLAM: a semantic SLAM based on enhanced fast-SCNN dynamic region detection and DeepFillv2-Driven background inpainting
Miao et al. Pseudo-lidar for visual odometry
Negi et al. Best of both worlds: hybrid SNN-ANN architecture for event-based optical flow estimation
Deng et al. Quantity-quality enhanced self-training network for weakly supervised point cloud semantic segmentation
Komatsu et al. Octave deep plane-sweeping network: reducing spatial redundancy for learning-based plane-sweeping stereo
CN115731607A (en) Performing occlusion-aware global 3D pose and shape estimation on articulated objects
Ma et al. Fov-net: Field-of-view extrapolation using self-attention and uncertainty
You et al. Enhancing low-light images for monocular visual odometry in challenging lighting conditions
Zhu et al. Self-Constructing Stereo Correspondences for Unsupervised Multi-View Stereo