3D Texture Atlas Rendering Loss for Unaligned Image Super-Resolution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current 3D reconstruction methods rely on 2D super-resolution processing techniques that are not applicable to unaligned image sequences, leading to inefficiencies in texture mapping and rendering quality, particularly in mobile applications.
Innovation Solution
A method involving a convolutional neural network (CNN) framework that incorporates rendering loss to enhance 3D texture mapping by comparing rendered images with ground truth images, allowing for super-resolution processing of 3D models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If 2D super-resolution processing techniques are applied to unaligned image sequences, then texture mapping may be performed, but rendering quality deteriorates due to misalignment and inefficiencies
Solution Approach 1:
The patent transitions from 2D super-resolution processing to 3D super-resolution processing by incorporating depth information and 3D geometric constraints. This dimensional change allows the system to process unaligned image sequences in 3D space, maintaining texture mapping quality while improving rendering reliability through proper 3D spatial relationships.
Solution Approach 2:
The patent changes the processing parameters from 2D image coordinates to 3D spatial coordinates including depth information. By modifying the parameter space to include 3D geometric constraints and depth maps, the system can properly handle unaligned images and maintain both texture mapping quality and rendering reliability.
2Reliability
If high-resolution input images are used for 3D reconstruction, then rendering quality improves, but memory and computational requirements increase
Solution Approach 1:
The patent segments the super-resolution processing into 3D spatial components, processing the model in three-dimensional space rather than requiring full high-resolution 2D images. This segmentation allows quality improvement while reducing resource consumption by working with 3D geometric representations and depth maps instead of complete high-resolution image data.
Solution Approach 2:
By moving to 3D processing, the patent reduces the dimensional data requirements. Instead of storing and processing large 2D high-resolution images, the system uses 3D geometric models with associated depth maps and texture coordinates, which require less memory and computational resources while maintaining or improving rendering quality.
3Ease of operation
If 3D reconstruction is performed with unaligned image sequences, then mobile application feasibility improves, but texture mapping efficiency deteriorates
Solution Approach 1:
The patent changes the approach by incorporating 3D geometric constraints and depth information as additional parameters. This allows the system to process unaligned images from mobile devices effectively, maintaining feasibility while improving texture mapping efficiency through proper 3D spatial registration and constraint-based optimization.
Data Source
AI summary
An electronic device and method for texturing a three dimensional (3D) model are provided. The method includes rendering a texture atlas to obtain a first set of two dimensional (2D) images of the 3D model; rendering a ground truth texture atlas to obtain a second set of 2D images of the 3D model; comparing the first set of images with the second set of images to determine a rendering loss; applying the texture sampling properties to a convolutional neural network (CNN) to incorporate the rendering loss into a deep learning framework; and inputting a 2D texture atlas into the CNN to generate a texture of the 3D module.