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

Xue et al., 2019 - Google Patents

Mvscrf: Learning multi-view stereo with conditional random fields

Xue et al., 2019

View PDF
Document ID
15652858469228321686
Author
Xue Y
Chen J
Wan W
Huang Y
Yu C
Li T
Bao J
Publication year
Publication venue
Proceedings of the IEEE/CVF International Conference on Computer Vision

External Links

Snippet

We present a deep-learning architecture for multi-view stereo with conditional random fields (MVSCRF). Given an arbitrary number of input images, we first use a U-shape neural network to extract deep features incorporating both global and local information, and then …
Continue reading at openaccess.thecvf.com (PDF) (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
    • 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
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • 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
    • 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
    • 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
    • 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
    • G06K9/20—Image acquisition
    • G06K9/34—Segmentation of touching or overlapping patterns in the image field
    • G06K9/342—Cutting or merging image elements, e.g. region growing, watershed, clustering-based techniques
    • 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
    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • 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
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • 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
    • G06F17/50—Computer-aided design
    • 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
    • G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image

Similar Documents

Publication Publication Date Title
Xue et al. Mvscrf: Learning multi-view stereo with conditional random fields
Cheng et al. Deep stereo using adaptive thin volume representation with uncertainty awareness
Xie et al. Linking points with labels in 3D: A review of point cloud semantic segmentation
Zhang et al. Cascaded context pyramid for full-resolution 3d semantic scene completion
Sigal Human pose estimation
Zhang et al. Geomvsnet: Learning multi-view stereo with geometry perception
Chen et al. Visibility-aware point-based multi-view stereo network
Li et al. ADR-MVSNet: A cascade network for 3D point cloud reconstruction with pixel occlusion
CN113378756B (en) Three-dimensional human body semantic segmentation method, terminal device and storage medium
CN109766866B (en) Face characteristic point real-time detection method and detection system based on three-dimensional reconstruction
Song et al. Contextualized CNN for scene-aware depth estimation from single RGB image
CN116129289A (en) Attention edge interaction optical remote sensing image saliency target detection method
CN113297959B (en) Target tracking method and system based on corner point attention twin network
CN116310098A (en) Multi-view three-dimensional reconstruction method based on attention mechanism and variable convolution depth network
Song et al. Prior depth-based multi-view stereo network for online 3D model reconstruction
CN112819832A (en) Urban scene semantic segmentation fine-grained boundary extraction method based on laser point cloud
Li et al. Monocular 3-D Object Detection Based on Depth-Guided Local Convolution for Smart Payment in D2D Systems
Lin et al. High-resolution multi-view stereo with dynamic depth edge flow
Song et al. Implicit neural refinement based multi-view stereo network with adaptive correlation
Liang et al. A novel deep network and aggregation model for saliency detection
Wang et al. Deep learning-based 3D reconstruction from multiple images: A survey
Zhu et al. Semantics and Contour Based Interactive Learning Network For Building Footprint Extraction
Forbes et al. Deep autoencoders with aggregated residual transformations for urban reconstruction from remote sensing data
Lee et al. Multi-scaled and densely connected locally convolutional layers for depth completion
Wang et al. Motion parallax estimation for ultra low altitude obstacle avoidance