Graphics Texture Reconstruction with Single-Grid Multi-Resolution Decoding
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Solution Overview
Problem
Existing graphics texture reconstruction techniques require storing texture data across multiple resolutions, leading to high storage and computational overhead, and lack flexibility in decoding resolutions.
Innovation Solution
A method involving an encoder that generates a compressed latent representation of textures as a grid of feature sets, allowing flexible reconstruction at various resolutions without needing multiple grid resolutions, using an encoder and decoder system with machine-learning components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If precomputed texture pyramids are stored to allow direct access to different resolutions, then texture access flexibility is improved, but storage overhead increases significantly
Solution Approach 1:
The texture data is segmented into a coarse grid structure where only essential texture information is stored at low resolution. This segmentation allows the system to avoid storing complete high-resolution texture data while still enabling access to multiple resolutions through selective sampling and reconstruction.
Solution Approach 2:
The patent transitions from storing textures in traditional multi-resolution pyramid format (2D resolution dimensions) to a grid-based latent space representation. This dimensional transformation allows textures to be reconstructed at various resolutions from a single compressed grid representation, reducing storage requirements while maintaining access flexibility.
2Adaptability or versatility
If grid-based techniques store grids at multiple resolutions for texture decoding, then decoding flexibility is improved, but storage requirements increase
Solution Approach 1:
A single grid representation at one resolution serves multiple functions: it can be used to reconstruct textures at the original resolution, at lower resolutions through sampling, and at higher resolutions through super-resolution techniques. This universal grid structure eliminates the need to store separate grids for each resolution level.
Solution Approach 2:
The system changes the resolution parameter dynamically during the reconstruction process rather than storing multiple fixed-resolution grids. By using a single grid representation and adjusting reconstruction parameters (sampling rates, interpolation methods), the system achieves multi-resolution capability without proportional increases in storage.
3Ease of operation
If specialized decoding architectures are used for random sampling from grids, then random access capability is improved, but device complexity increases
Solution Approach 1:
The grid structure and sampling mechanism are designed to be self-sufficient for random access operations. The system uses simple coordinate-based indexing into the grid followed by standard sampling operations, eliminating the need for complex specialized decoding architectures. The grid itself provides the random access capability through its structured layout.
4Manufacturing precision
If learning-based approaches jointly optimize decoder parameters and data representations, then reconstruction quality is improved, but computational overhead increases
Solution Approach 1:
The patent extracts and separates the optimization of grid representation from the optimization of decoder parameters. By fixing the grid encoding approach and only optimizing the simple sampling and reconstruction parameters, the system achieves good reconstruction quality with significantly reduced computational overhead compared to joint optimization of all parameters.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for reconstructing a texel of a texture. Such techniques may include receiving a plurality of sets of features corresponding to the texture, wherein the plurality of sets of features comprises a respective set of features for each respective grid point of a grid; receiving coordinate information corresponding to the texel of the texture; receiving level of detail information; selecting a subset of grid points of the grid based on the second resolution being lower than the first resolution; sampling one or more grid points from among the subset of grid points based on the coordinate information to obtain sampled features associated with the one or more grid points; inputting, to a machine-learning model, the sampled features; and receiving, from the machine-learning model, based on the sampled features, a reconstruction of the texel of the texture at the second resolution.