Texture Atlas Packing via Infinity Norm Compactness
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Solution Overview
Problem
Current texture mapping methods in computer graphics result in inefficient packing of texture charts into texture atlases, leading to wasted space and suboptimal texture resolution due to the use of rectangular bounding boxes and Euclidean distance minimization, which do not account for the arbitrary shapes of texture charts.
Innovation Solution
A texture atlas generating engine evaluates compactness scores for candidate placements of texture charts based on their distance to the center of mass of previously incorporated charts, using the infinity norm to minimize wasted space and optimize packing, thereby growing the texture atlas in a near-square shape.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If rectangular bounding boxes are used to approximate texture charts, then the packing process is simplified, but unused space increases and texture atlas size grows
Solution Approach 1:
The patent segments the texture chart placement problem into multiple candidate positions around previously placed charts. Instead of using a single bounding box approximation, it evaluates multiple discrete placement candidates at specific distances (e.g., edge-to-edge distances) from existing charts, selecting the optimal position that minimizes wasted space while maintaining arbitrary shape integrity.
Solution Approach 2:
The patent transitions from two-dimensional bounding box approximation to considering placement in the conceptual space of distances and orientations. By evaluating candidate positions based on edge-to-edge distances and angular orientations relative to previously placed charts, it optimizes packing in a multi-dimensional parameter space rather than simple rectangular coordinates.
2Ease of manufacture
If Euclidean distance minimization is used for placing texture charts, then the placement criterion is straightforward, but unused space within the texture atlas increases
Solution Approach 1:
The patent applies local quality by evaluating placement candidates based on their specific local geometry and orientation relative to neighboring charts. Instead of uniform Euclidean distance treatment, it considers edge-to-edge distances and angular relationships that are locally optimal for each placement position, adapting the placement criterion to the specific geometric context of each candidate location.
Solution Approach 2:
The patent changes the placement evaluation parameter from simple Euclidean distance to a composite criterion involving edge-to-edge distances, angular orientations, and compactness scores. This parameter transformation allows for more nuanced optimization of packing efficiency while maintaining computational tractability through systematic evaluation of modified distance metrics.
3Ease of manufacture
If texture charts are placed from only the top boundary, then the placement process is simple, but compactness and unused space optimization are limited
Solution Approach 1:
The patent segments the placement evaluation into multiple directional approaches by considering candidate positions from all four sides (top, bottom, left, right) of previously placed charts. This segmentation allows systematic exploration of placement options in different orientations, improving compactness while maintaining a structured evaluation process.
Solution Approach 2:
The patent extends the placement evaluation from a single directional approach to multi-dimensional spatial reasoning by evaluating candidate positions in all four cardinal directions. This dimensional expansion allows the algorithm to discover optimal placements that would be missed by unidirectional evaluation, achieving better compactness scores.
4Quantity of substance
If the texture atlas grows in arbitrary shape, then all texture charts can be packed, but storage and processing efficiency decreases
Solution Approach 1:
The patent employs asymmetric growth control by evaluating candidate placements based on their contribution to overall atlas compactness. The compactness score criterion favors placements that maintain a more balanced, near-square atlas shape, introducing asymmetric penalties for placements that would create excessive irregularity or elongation in the atlas boundary.
Solution Approach 2:
The patent changes the shape control parameter by incorporating compactness scores into the placement evaluation criterion. This parameter modification transforms the growth behavior from arbitrary shape expansion to controlled growth that maintains efficiency, using the compactness metric as a regulatory parameter that balances capacity utilization with storage/processing efficiency.
Data Source
AI summary
Embodiments of the present invention are directed towards compactly incorporating texture charts into a texture atlas. Texture charts represent three-dimensional mesh segments flattened into two-dimensional shapes. In one embodiment, a texture atlas generating engine is used to generate and evaluate compactness scores of candidate placements for a texture chart. Candidate placements generally refer to the possible locations where a texture chart can be incorporated into a texture atlas. The compactness score can be based on minimizing the distance between a texture chart being incorporated into the texture atlas and the center of mass of previously incorporated texture charts within a texture atlas. In embodiments, an infinity norm can be utilized to compute such a compactness score by outputting an average length of vectors between a texture chart being incorporated into a texture atlas and the texture atlas. Other embodiments may be described and/or claimed.