Volume Data Rendering via Adaptive Sub-Volume Sampling
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Solution Overview
Problem
Current volume rendering methods require extensive calculations and time to handle non-uniform volume data with multi-resolution, as they need to reconstruct data to have uniform resolution for the entire volume.
Innovation Solution
The proposed solution involves dividing volume data into sub-volumes based on resolution or distance from the viewpoint, determining sampling intervals for each sub-volume, and sampling these sub-volumes along a ray path to synthesize rendering values for pixels, thereby avoiding the need for uniform resolution reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If volume data is reconstructed to have uniform resolution throughout the entire volume, then rendering consistency is improved, but computational time and processing load increase significantly
Solution Approach 1:
The volume data is divided into multiple sub-volumes with different resolution levels based on their distance from the viewpoint. This segmentation allows each sub-volume to be processed independently with appropriate sampling intervals, avoiding the need to reconstruct the entire volume at uniform high resolution and thus reducing computational time while maintaining rendering consistency in visible regions.
Solution Approach 2:
Different resolution levels are assigned to different spatial regions of the volume data based on their distance from the viewpoint. Sub-volumes closer to the viewpoint are rendered with higher resolution and smaller sampling intervals, while distant sub-volumes use lower resolution and larger sampling intervals. This local quality approach ensures that computational resources are focused on regions that contribute most to the final image quality.
2Measurement precision
If the entire volume data is processed at high resolution, then image quality is improved, but processing complexity and time consumption increase
Solution Approach 1:
The sampling interval is dynamically adjusted based on the distance of each sub-volume from the viewpoint. This dynamic adaptation allows the system to automatically allocate processing resources efficiently, using smaller sampling intervals (higher precision) for nearby sub-volumes that require greater detail, and larger sampling intervals (lower precision) for distant sub-volumes, thereby reducing overall processing complexity while maintaining high image quality in critical regions.
3Ease of operation
If uniform sampling intervals are used across all volume data, then processing simplicity is maintained, but rendering accuracy for multi-resolution data deteriorates
Solution Approach 1:
The sampling interval parameter is changed adaptively for each sub-volume based on its resolution level and distance from the viewpoint. This parameter change allows the system to optimize rendering accuracy for each region independently, using smaller intervals for high-resolution nearby sub-volumes and larger intervals for low-resolution distant sub-volumes, thereby achieving high rendering accuracy without requiring uniformly simple processing across the entire volume.
Data Source
AI summary
A method renders volume data having multi-resolution. The volume data is divided into a plurality of sub-volumes according to a resolution, a sampling interval for each of the plurality of sub-volumes is determined based on resolutions of the plurality of sub-volumes, sub-volumes present in a path of a ray passing through each of pixels on a projection plane onto which the volume data is projected from one viewpoint are sampled according to the sampling intervals thereof, and a rendering value of each of the pixels on the projection plane is obtained by synthesizing a plurality of sampling values corresponding to each of the pixels from among sampling values obtained through the sampling.