Radiance Transfer Sampling for Augmented Reality
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Solution Overview
Problem
Current augmented reality (AR) solutions face computational inefficiencies in photorealistic rendering due to the high cost of computing radiance transfer (RT) for dynamically changing scenes, leading to undesirable aliasing artifacts from regular subsampling.
Innovation Solution
The implementation of adaptive sampling, filtering, and reconstruction methods that exploit 4-D spatial and temporal coherence, allowing for more controlled trade-offs between computational expense and image quality, particularly in mobile devices, by determining subsets of rays for refinement in shadow areas and edges, and utilizing a two-level hierarchical cache for temporal coherence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If volumetric raytracing is applied to compute radiance transfer for all visible surface points in dynamically changing scenes, then photorealistic rendering quality is achieved, but computational expense increases significantly
Solution Approach 1:
The patent segments the scene into static and dynamic components, applying different processing strategies to each. Static geometry is preprocessed and cached, while only dynamic elements require full radiance transfer computation. This segmentation reduces the overall computational burden while maintaining photorealistic quality where needed.
Solution Approach 2:
The patent applies adaptive sampling that concentrates computational effort in regions requiring higher quality (such as shadow boundaries and occlusion edges) while using coarser sampling in less critical areas. This local quality approach maintains photorealistic rendering where it matters most while reducing overall computational expense.
2Productivity
If regular subsampling is used to reduce computational cost, then interactive speeds are achieved, but aliasing artifacts appear in the rendered images
Solution Approach 1:
The patent implements dynamic adaptive sampling that adjusts sampling density based on scene content, motion detection, and error metrics. This dynamic approach maintains interactive speeds by reducing sampling in stable regions while automatically increasing sampling in regions exhibiting aliasing or high visual importance, thus preventing artifacts.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor rendered output for aliasing artifacts and adjust sampling parameters accordingly. Error metrics from previous frames guide the distribution of sampling effort in current frames, creating a feedback loop that maintains image quality while preserving interactive performance.
3Manufacturing precision
If radiance transfer is computed for every surface point in view, then photorealistic AR images are generated, but computational time increases making real-time processing difficult
Solution Approach 1:
The patent performs preliminary processing by precomputing radiance transfer for static geometry and caching results for reuse across multiple frames. This preliminary action eliminates redundant computations in subsequent frames, significantly reducing computational time while maintaining photorealistic quality through the use of precomputed data.
Solution Approach 2:
The system discards redundant computations by identifying and caching radiance transfer results that remain valid across multiple frames. When scene or camera changes occur, only the affected regions are recomputed, while other regions recover and reuse previously computed data, dramatically reducing overall computational time.
Data Source
AI summary
Methods, systems, computer-readable media, and apparatuses for radiance transfer sampling for augmented reality are presented. In some embodiments, a method includes receiving at least one video frame of an environment. The method further includes generating a surface reconstruction of the environment. The method additionally includes projecting a plurality of rays within the surface reconstruction of the environment. Upon projecting a plurality of rays within the surface reconstruction of the environment, the method includes generating illumination data of the environment from the at least one video frame. The method also includes determining a subset of rays from the plurality of rays in the environment based on areas within the environment needing refinement. The method further includes rendering the virtual object over the video frames based on the plurality of rays excluding the subset of rays.