Cost-Driven Framework for Progressive Mesh Compression
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Solution Overview
Problem
Conventional approaches to compressing triangular mesh data lack flexibility, resulting in suboptimal decoding experiences due to inflexible LOD reduction processes, which may lead to less faithful representations of three-dimensional objects during transfer and display.
Innovation Solution
A cost-driven framework is implemented that optimizes the LOD reduction process by defining a cost metric predicting computing resources needed for decoding and rendering, selecting the LOD reduction process at each step based on this metric, and evaluating the resulting LODs to improve the rate-distortion tradeoff.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional LOD reduction processes are used, then compression is achieved, but flexibility is lost and decoding experience deteriorates
Solution Approach 1:
The patent implements dynamic selection of LOD reduction processes at each reduction step based on a cost metric that predicts computing resources. Instead of using a fixed conventional process, the system adapts by evaluating multiple candidate processes and selecting the one that optimizes the balance between compression efficiency and decoding quality, thereby resolving the contradiction between flexibility and reliability.
Solution Approach 2:
The patent changes the parameter of selecting different LOD reduction processes dynamically. By introducing a cost metric that evaluates computing resource requirements and using this metric to guide process selection, the system transforms the static parameter selection into a dynamic optimization process, improving both flexibility and decoding experience simultaneously.
2Quantity of substance
If aggressive LOD reduction is performed, then data size is reduced, but representation fidelity deteriorates
Solution Approach 1:
The patent introduces feedback through a cost metric that evaluates the computing resource requirements and quality impact of each LOD reduction step. This feedback mechanism allows the system to adjust the reduction process dynamically, selecting operations that achieve data size reduction while maintaining representation fidelity by avoiding excessive compression that would degrade quality.
Solution Approach 2:
The patent applies partial action by selecting LOD reduction processes that achieve sufficient compression without over-compressing. The cost metric guides the selection to stop or adjust the reduction when the marginal gain in data size reduction no longer justifies the loss in representation fidelity, thereby optimizing the tradeoff between these two parameters.
3Reliability
If multiple LOD reduction processes are evaluated, then optimal selection is achieved, but computational overhead increases
Solution Approach 1:
The patent implements self-service by using the cost metric to automatically evaluate and select the optimal LOD reduction process without requiring external intervention or complex optimization algorithms. The system serves itself by using the predicted computing resource requirements to guide the selection, thereby achieving optimization quality while minimizing the additional computational overhead of the evaluation process.
4Manufacturing precision
If cost metric evaluation is performed at each step, then LOD selection is optimized, but processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-defining the cost metric and the set of candidate LOD reduction processes before the actual LOD reduction begins. This preparation allows the system to quickly evaluate and select optimal processes during the reduction steps without performing complex analyses in real-time, thereby achieving accurate LOD selection while minimizing processing time overhead.
Data Source
AI summary
Techniques of compressing level of detail (LOD) data involve defining a cost metric that predicts how much computing resources are necessary to decode and render a mesh at a given LOD. The cost metric may be optimized by a selection of a LOD reduction process of a plurality of processes at each LOD reduction step. For each process of the plurality of processes, the LOD is reduced according to that process and the resulting reduced LOD is evaluated according to the cost metric. Each such process at that LOD reduction step produces a respective LOD, which includes a mesh, one or more texture atlases, and/or other attributes. The LOD produced by the process having the lowest value of the cost metric at a reduction step is the LOD that is input into the next LOD reduction step.