Anisotropic Mip-map Construction via World Space Downsampling
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Solution Overview
Problem
Existing volume rendering techniques face challenges with anisotropic datasets, where voxel sizes and numbers along the three major axes are different, leading to computationally expensive rendering and aliasing artifacts when using power-of-two down-sampling for mip-map construction.
Innovation Solution
Down-sampling in world space using arbitrary scale factors rather than powers of two, allowing non-uniform reduction in voxels along different dimensions to create more isotropic mip-maps, which optimizes voxel count and memory usage while enabling efficient rendering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If power-of-two down-sampling is used for mip-map construction, then interpolation between adjacent mip-map levels is facilitated, but rendering is computationally expensive and aliasing artifacts occur with anisotropic datasets
Solution Approach 1:
The patent changes the down-sampling parameter from fixed power-of-two factors to arbitrary scale factors that adapt to the dataset's aspect ratio. This allows the mip-map construction to optimize for rendering efficiency while maintaining interpolation quality, resolving the contradiction between ease of interpolation and rendering productivity.
Solution Approach 2:
The patent introduces dynamic scale factors that adjust based on the anisotropic characteristics of the dataset. Instead of static power-of-two down-sampling, the system dynamically determines appropriate scaling factors along different axes to create isotropic mip-maps, improving rendering performance while maintaining interpolation quality.
2Stability of the object's composition
If power-of-two down-sampling is used for mip-map construction, then mip-map levels are created systematically, but anisotropic datasets produce similarly anisotropic mip-maps leading to aliasing artifacts
Solution Approach 1:
The patent changes the down-sampling parameters from fixed power-of-two factors to arbitrary scale factors calculated based on the dataset's aspect ratio. This transformation allows the mip-maps to become isotropic even when the original dataset is anisotropic, eliminating aliasing artifacts while maintaining systematic mip-map structure.
Solution Approach 2:
The patent applies asymmetric down-sampling factors along different axes to compensate for the anisotropy in the original dataset. By using different scale factors for each dimension based on the aspect ratio, the system transforms the asymmetric (anisotropic) input into symmetric (isotropic) mip-maps, preventing aliasing artifacts.
3Productivity
If down-sampling by power of two is used, then rendering speed may improve, but voxel distribution remains suboptimal for anisotropic datasets
Solution Approach 1:
The patent optimizes voxel distribution by changing the down-sampling parameters from fixed power-of-two factors to arbitrary scale factors. This allows the system to achieve better rendering speed while simultaneously optimizing voxel distribution for anisotropic datasets, eliminating the need to trade off between speed and optimization complexity.
Data Source
AI summary
Mip-map construction is provided for three-dimensional rendering from an anisotropic dataset. One or more mip-map levels are generated by down sampling in the world space rather than texture space. The down sampling may be by an arbitrary scale factor rather than a power of two. For example, the voxels may be down sampled along one dimension by less than half (e.g., 16 to 12). The scale factors may be different along different dimensions. This non-uniform reduction in voxels may result in the mip-map being more isotropic than the anisotropic dataset.