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Adaptable 2D to 3D Stereo Vision Image Conversion Based on a Deep Convolutional Neural Network and Fast Inpaint

Tomasz Hachaj1

  • 1Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Krakow, Al. Mickiewicza 30, 30-059 Krakow, Poland.

Entropy (Basel, Switzerland)
|August 26, 2023
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Summary
This summary is machine-generated.

This study introduces a fast and effective algorithm for converting 2D to 3D content, crucial for virtual reality systems. The novel approach enhances depth image-based rendering (DIBR) with a rapid inpainting technique, maintaining high visual quality.

Keywords:
2D to 3DDIBRconvolutional neural networkdepthdepth image based renderingdisparitymonocular stereo reconstructionstereoscopy

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Area of Science:

  • Computer Vision
  • Image Processing
  • Virtual Reality

Background:

  • The decline of 3D TV production has increased demand for 2D to 3D conversion methods.
  • Virtual reality systems rely heavily on stereo vision, driving the need for advanced 3D rendering.

Purpose of the Study:

  • To propose and validate novel depth image-based rendering (DIBR) approaches for 2D to 3D conversion.
  • To introduce a significantly faster inpainting algorithm (FAST) without compromising image quality.
  • To develop a user-adjustable parameter for controlling DIBR visualization.

Main Methods:

  • Utilized state-of-the-art single-frame depth generation neural networks and inpainting algorithms.
  • Developed a novel very fast inpainting (FAST) algorithm to fill missing pixels in stereo pairs.
  • Proposed a single adaptable parameter to govern camera parameters and binocular disparity for DIBR.

Main Results:

  • The FAST inpainting algorithm demonstrated superior speed compared to existing methods, with no degradation in output quality.
  • The proposed DIBR solution, integrating MiDaS and FAST, was highly acclaimed by evaluators.
  • The mean absolute error of the proposed method showed no significant difference from state-of-the-art approaches.

Conclusions:

  • The developed 2D to 3D conversion algorithm offers an efficient and high-quality solution for virtual reality applications.
  • The intuitive disparity steering and FAST inpainting provide a robust and reproducible method for video and image conversion.
  • Open-source code availability facilitates further research and application of the proposed techniques.