Spatial Frame Gaussian Synthesis for Depth Discontinuities
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Solution Overview
Problem
Current techniques do not optimize Gaussians for synthesizing spatial videos effectively, leading to resource-intensive processing and inefficient handling of depth discontinuities in graphics rendering.
Innovation Solution
A method involving a graphics processor that determines and transmits a subset of optimized Gaussians based on display pose to handle depth discontinuities, using Gaussian splatting techniques to minimize resource usage by focusing on regions of interest with sharp depth discontinuities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional Gaussian synthesis techniques are used for spatial frames, then complete scene coverage is achieved, but memory and compute requirements become excessively high
Solution Approach 1:
The patent segments the scene into multiple depth planes based on depth discontinuities. Instead of processing the entire scene uniformly, it divides the spatial frame into foreground and background segments, allowing selective Gaussian synthesis only in regions with significant depth changes. This segmentation reduces the overall computational burden while maintaining scene coverage completeness.
Solution Approach 2:
The patent applies Gaussian synthesis selectively only in regions with sharp depth discontinuities rather than uniformly across the entire scene. By identifying and processing only the local regions where depth changes occur, the system maintains high quality where needed while reducing memory and compute resources in regions where full synthesis is unnecessary.
2Manufacturing precision
If Gaussian optimization is applied to entire scenes, then rendering quality is maintained, but processing efficiency decreases
Solution Approach 1:
The patent applies Gaussian optimization locally only in regions with sharp depth discontinuities rather than across the entire scene. This localized approach maintains rendering quality in critical areas while significantly improving processing efficiency by avoiding unnecessary computations in regions where depth is relatively uniform.
Solution Approach 2:
The patent applies Gaussian synthesis partially, focusing computational effort only on regions with depth discontinuities rather than applying it excessively across the entire scene. This partial action approach maintains sufficient rendering quality for perceptually important regions while improving overall processing efficiency.
3Reliability
If all Gaussians are transmitted to handle depth discontinuities, then disocclusion handling is complete, but bandwidth and memory usage increase
Solution Approach 1:
The patent extracts and transmits only the subset of Gaussians that are relevant to handling depth discontinuities and disocclusions, rather than transmitting all Gaussians. By identifying and extracting only the necessary Gaussian parameters for regions with depth changes, the system maintains complete disocclusion handling while reducing bandwidth and memory usage.
Solution Approach 2:
The patent segments the Gaussian set into relevant and irrelevant portions based on depth discontinuity analysis. Only the segmented portion containing Gaussians for regions with depth changes is transmitted, ensuring complete disocclusion handling where needed while minimizing bandwidth and memory consumption.
Data Source
AI summary
This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for synthesizing spatial videos using Gaussian models. A first graphics processor (e.g., at a server) may obtain a set of frames. The first graphics processor may determine a set of Gaussians based on the set of frames. A second graphics processor (e.g., at a client), may transmit a request for a set of Gaussians. The first graphics processor may receive the request for at least a subset of the set of Gaussians. The first graphics processor may transmit an indication of at least the subset of the set of Gaussians in response to the request. The second graphics processor may receive the set of Gaussians in response to a transmission of the request. The second graphics processor may perform alpha composition based on the received set of Gaussians and a depth-based projection.