Sparse Point Cloud Rendering for Headset-Free 3D Displays
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Solution Overview
Problem
Current technologies for untethered head-mounted displays and goggleless 3D displays face challenges in rendering high-quality images efficiently, as they require significant computing power, and projector-based AR solutions are limited by the need for headset usage and inability to project content in opposite directions.
Innovation Solution
A method involving a content server and viewing client that share the workload of real-time ray tracing by distributing sparse point cloud samples with varying viewing directions based on material characteristics, allowing for local rendering and increased sampling density to enhance image quality without the need for a headset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full view rendering at uniformly high sample density is performed, then image quality is improved, but data volume and computing power requirements increase significantly
Solution Approach 1:
The patent applies local quality by rendering different regions of the view with different sample densities based on their importance. The foveal region (center of gaze) receives high sample density for maximum quality, while peripheral regions receive lower sample density. This resolves the contradiction by concentrating computational resources where they provide the most perceptual benefit, reducing overall data volume while maintaining perceived image quality.
Solution Approach 2:
The patent uses partial action by rendering only the necessary portion of the full view at high quality. Instead of uniformly rendering the entire field of view at maximum sample density, the system selectively renders the foveal region with excessive sampling (more than needed for peripheral areas) while using reduced sampling elsewhere, optimizing the balance between image quality and data volume.
2Measurement precision
If ray tracing is used for high quality rendering, then image quality is improved, but graphics processing performance requirements increase to high end desktop levels
Solution Approach 1:
The patent applies local quality by performing computationally intensive ray tracing only in the foveal region where high image quality is most perceptible, while using less demanding rendering techniques for peripheral regions. This resolves the contradiction by concentrating high-performance graphics processing only where it provides maximum perceptual benefit, reducing overall power requirements.
Solution Approach 2:
The patent segments the rendering task into different regions (foveal and peripheral) with different computational requirements. By dividing the view into segments and applying appropriate rendering techniques to each, the system reduces the overall graphics processing burden while maintaining high quality where it matters most, resolving the contradiction between image quality and processing power requirements.
3Extent of automation
If more computing is performed by the HMD itself, then integration and image quality are improved, but device complexity and power consumption increase
Solution Approach 1:
The patent reduces device complexity by performing complex computations locally only where needed (foveal region) while simplifying or omitting computations in peripheral regions. This allows the HMD to maintain high integration and image quality in the critical viewing area without requiring the entire device to be equally complex and power-intensive throughout.
Data Source
AI summary
Systems and methods are described for rendering a 3D synthetic scene. A display client receives point cloud samples of the scene, where the point cloud samples include a point location, one or more viewing directions, and color information for each of the viewing directions. The point cloud samples may be generated by a server using ray tracing. The display client combines information from the point cloud samples with a locally generated rendering of at least a portion of the scene to generate a combined rendering of the scene, and the client displays the combined rendering. The number of point cloud samples may be adjusted adaptively based on performance metrics at the client.