PIINET: A 360-degree Panoramic Image Inpainting Network Using a Cube Map

Autor: Doug Young Suh, Seo Woo Han
Jazyk: angličtina
Rok vydání: 2020
Předmět:
FOS: Computer and information sciences
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Inpainting
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Computer Science - Computer Vision and Pattern Recognition
02 engineering and technology
Field (computer science)
Biomaterials
Discriminative model
0202 electrical engineering
electronic engineering
information engineering
FOS: Electrical engineering
electronic engineering
information engineering
Computer vision
Electrical and Electronic Engineering
business.industry
Distortion (optics)
Deep learning
Image and Video Processing (eess.IV)
020207 software engineering
Electrical Engineering and Systems Science - Image and Video Processing
Cube mapping
Computer Science Applications
Mechanics of Materials
Modeling and Simulation
Face (geometry)
Equirectangular projection
020201 artificial intelligence & image processing
Artificial intelligence
business
Popis: Inpainting has been continuously studied in the field of computer vision. As artificial intelligence technology developed, deep learning technology was introduced in inpainting research, helping to improve performance. Currently, the input target of an inpainting algorithm using deep learning has been studied from a single image to a video. However, deep learning-based inpainting technology for panoramic images has not been actively studied. We propose a 360-degree panoramic image inpainting method using generative adversarial networks (GANs). The proposed network inputs a 360-degree equirectangular format panoramic image converts it into a cube map format, which has relatively little distortion and uses it as a training network. Since the cube map format is used, the correlation of the six sides of the cube map should be considered. Therefore, all faces of the cube map are used as input for the whole discriminative network, and each face of the cube map is used as input for the slice discriminative network to determine the authenticity of the generated image. The proposed network performed qualitatively better than existing single-image inpainting algorithms and baseline algorithms.
Databáze: OpenAIRE
Externí odkaz: