Nothing Special   »   [go: up one dir, main page]

Sharma et al., 2020 - Google Patents

Nighttime stereo depth estimation using joint translation-stereo learning: Light effects and uninformative regions

Sharma et al., 2020

View PDF
Document ID
4243819241120844346
Author
Sharma A
Cheong L
Heng L
Tan R
Publication year
Publication venue
2020 International Conference on 3D Vision (3DV)

External Links

Snippet

Nighttime stereo depth estimation is still challenging, as assumptions associated with daytime lighting conditions do not hold any longer. Nighttime is not only about lowlight and dense noise, but also about glow/glare, flares, non-uniform distribution of light, etc. One of …
Continue reading at arxiv.org (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
    • 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
    • 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
    • G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
    • 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
    • G06T11/00—2D [Two Dimensional] image generation
    • 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
    • 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
    • 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

Similar Documents

Publication Publication Date Title
Li et al. Monocular depth estimation with hierarchical fusion of dilated cnns and soft-weighted-sum inference
Luo et al. Consistent video depth estimation
Jaritz et al. Sparse and dense data with cnns: Depth completion and semantic segmentation
Sharma et al. Nighttime stereo depth estimation using joint translation-stereo learning: Light effects and uninformative regions
Huang et al. Indoor depth completion with boundary consistency and self-attention
Zhang et al. Multi-scale single image dehazing using perceptual pyramid deep network
Georgoulis et al. Reflectance and natural illumination from single-material specular objects using deep learning
Bonneel et al. Intrinsic decompositions for image editing
Georgoulis et al. What is around the camera?
Bescos et al. Empty cities: Image inpainting for a dynamic-object-invariant space
CN110120049B (en) Method for jointly estimating scene depth and semantics by single image
Cong et al. Discrete haze level dehazing network
Atapour-Abarghouei et al. Generative adversarial framework for depth filling via wasserstein metric, cosine transform and domain transfer
Bischke et al. Overcoming missing and incomplete modalities with generative adversarial networks for building footprint segmentation
Lee et al. CNN-based simultaneous dehazing and depth estimation
Zhang et al. DuGAN: An effective framework for underwater image enhancement
Li et al. Self-supervised coarse-to-fine monocular depth estimation using a lightweight attention module
Yang et al. Underwater self-supervised depth estimation
Polasek et al. Vision UFormer: Long-range monocular absolute depth estimation
Song et al. Half‐body Portrait Relighting with Overcomplete Lighting Representation
Khan et al. Towards monocular neural facial depth estimation: Past, present, and future
Song et al. Artificial Intelligence‐Assisted Fresco Restoration with Multiscale Line Drawing Generation
Haji-Esmaeili et al. Large-scale monocular depth estimation in the wild
Wei et al. Enforcing high frequency enhancement in deep networks for simultaneous depth estimation and dehazing
Wu Data augmentation