Light Field Volume Rendering via Hogel Partitioning and Caching
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Solution Overview
Problem
Current light field volume rendering techniques face challenges in achieving good resolution at acceptable working speeds due to high computational intensity and memory bandwidth bottlenecks.
Innovation Solution
The method involves partitioning a 3D scene into layers with associated light fields and slab volumes, performing volume rendering for rays intersecting slab volumes, caching volume data, and blending rendered light fields to produce a single output image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional volume rendering techniques are used for light field rendering, then image quality can be maintained, but computational intensity increases and memory bandwidth bottlenecks occur
Solution Approach 1:
The volume data is partitioned into multiple layers, and the light field is divided into hogel subsets that intersect specific slab volumes. This segmentation allows independent processing of different layers and subsets, reducing the computational burden on any single processing unit while maintaining overall image quality through composite rendering.
Solution Approach 2:
Volume data that intersects with slab volumes is cached in advance in local store cache memory before rendering operations. This preliminary action reduces memory bandwidth bottlenecks during the actual rendering process by avoiding repeated reads from slower memory sources, thereby improving rendering speed without compromising image quality.
2Measurement precision
If more rays are used for light field volume rendering, then resolution improves, but computational intensity and memory bandwidth requirements increase
Solution Approach 1:
Multiple rays that intersect the same slab volume are merged into hogel subsets for joint processing. By combining rays that share common volume data intersections, the system reduces redundant computations and memory accesses, achieving better resolution through coordinated ray processing while lowering overall computational intensity.
Solution Approach 2:
Different hogel subsets are processed with tailored rendering approaches based on their specific intersection characteristics with slab volumes. This local optimization allows the system to allocate computational resources more efficiently, focusing detailed processing only where needed while using simplified approaches elsewhere, thereby improving resolution in critical areas without uniformly increasing computational intensity across the entire scene.
Data Source
AI summary
A system and method for volume rendering a light field, wherein the light field data is subjected to a layering scheme introducing a partitioning of the hogels into subsets. Each subset corresponding to a sub-volume of the layer volume, corresponds to the sub-region of the layer. Novel partitioning of the data combined with an efficient local memory caching technique, plenoptic downsampling strategies to reduce memory bandwidth requirements and volume rendering algorithm to produce a rendered light field image. A reduction in the total number of samples required can be obtained while still maintaining the quality of the resulting image. A method is also provided to order memory accesses aligned with ray calculations in order to maximize access coherency. Real-time layered scene decomposition can be combined with surface rendering method to create a hybrid real-time rendering method that supports rendering of scenes containing superimposed volumes and surfaces.