18055584. MODIFYING TWO-DIMENSIONAL IMAGES UTILIZING THREE-DIMENSIONAL MESHES OF THE TWO-DIMENSIONAL IMAGES simplified abstract (ADOBE INC.)
Contents
- 1 MODIFYING TWO-DIMENSIONAL IMAGES UTILIZING THREE-DIMENSIONAL MESHES OF THE TWO-DIMENSIONAL IMAGES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MODIFYING TWO-DIMENSIONAL IMAGES UTILIZING THREE-DIMENSIONAL MESHES OF THE TWO-DIMENSIONAL IMAGES - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
MODIFYING TWO-DIMENSIONAL IMAGES UTILIZING THREE-DIMENSIONAL MESHES OF THE TWO-DIMENSIONAL IMAGES
Organization Name
Inventor(s)
Radomir Mech of Mountain View CA (US)
Nathan Carr of San Jose CA (US)
Matheus Gadelha of San Jose CA (US)
MODIFYING TWO-DIMENSIONAL IMAGES UTILIZING THREE-DIMENSIONAL MESHES OF THE TWO-DIMENSIONAL IMAGES - A simplified explanation of the abstract
This abstract first appeared for US patent application 18055584 titled 'MODIFYING TWO-DIMENSIONAL IMAGES UTILIZING THREE-DIMENSIONAL MESHES OF THE TWO-DIMENSIONAL IMAGES
Simplified Explanation
The disclosed patent application describes a system for generating three-dimensional meshes representing two-dimensional images for editing purposes. The system utilizes neural networks to determine density values of pixels, sample points in the image, generate a tessellation, estimate camera parameters, and modify the mesh based on the camera parameters.
- Neural networks are used to determine density values of pixels in a two-dimensional image based on estimated disparity.
- Points in the image are sampled according to the density values to generate a tessellation.
- A second neural network is used to estimate camera parameters and modify the three-dimensional mesh based on these parameters.
- The system can also modify a two-dimensional image based on a displacement input by mapping the mesh to the image, responding to the input, and updating the image accordingly.
Potential Applications
This technology could be applied in various fields such as:
- Computer graphics
- Virtual reality
- Augmented reality
- Image editing software
Problems Solved
This technology helps in:
- Enhancing the editing capabilities of two-dimensional images
- Providing a more intuitive way to modify images in three dimensions
- Improving the accuracy of estimating camera parameters
Benefits
The benefits of this technology include:
- Streamlining the editing process for images
- Allowing for more realistic modifications to images
- Enhancing the user experience in editing software
Potential Commercial Applications
A potential commercial application for this technology could be:
- Integration into popular image editing software to provide advanced 3D editing capabilities
Possible Prior Art
One possible prior art for this technology could be:
- Existing 3D modeling software that allows for the manipulation of 3D objects based on 2D images.
Unanswered Questions
How does this technology compare to existing image editing software?
This technology offers a more advanced way to edit images in three dimensions compared to traditional 2D editing software. It provides a more intuitive and realistic approach to modifying images.
What are the limitations of this technology in terms of image size and complexity?
The system may face limitations in processing large or complex images due to computational constraints. Additionally, the accuracy of the generated meshes may decrease with highly detailed or intricate images.
Original Abstract Submitted
Methods, systems, and non-transitory computer readable storage media are disclosed for generating three-dimensional meshes representing two-dimensional images for editing the two-dimensional images. The disclosed system utilizes a first neural network to determine density values of pixels of a two-dimensional image based on estimated disparity. The disclosed system samples points in the two-dimensional image according to the density values and generates a tessellation based on the sampled points. The disclosed system utilizes a second neural network to estimate camera parameters and modify the three-dimensional mesh based on the estimated camera parameters of the pixels of the two-dimensional image. In one or more additional embodiments, the disclosed system generates a three-dimensional mesh to modify a two-dimensional image according to a displacement input. Specifically, the disclosed system maps the three-dimensional mesh to the two-dimensional image, modifies the three-dimensional mesh in response to a displacement input, and updates the two-dimensional image.
