Hand-Based Light Estimation for Low-Latency XR Rendering
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Solution Overview
Problem
Existing XR technologies face challenges in accurately estimating lighting conditions for seamless integration of virtual content with the real world, particularly in head-worn devices, due to computational intensity and dynamic nature of real-world scenes, leading to disrupted XR experiences.
Innovation Solution
Utilizing the user's hand as a dynamic light probe, capturing hand images to estimate illumination parameters through trained machine learning models, allowing for efficient and accurate lighting estimation by selecting models based on detected hand gestures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional light estimation methods are used in head-worn XR devices, then lighting accuracy for virtual content integration is improved, but computational intensity and processing time increase
Solution Approach 1:
The patent extracts the light probe functionality from the entire scene processing and concentrates it on a specific object (the user's hand). By isolating the hand as the light estimation target, the system reduces computational complexity while maintaining lighting accuracy, directly resolving the contradiction between measurement precision and power consumption.
Solution Approach 2:
The system uses machine learning models trained on synthetic hand images to create a computational copy of the light estimation process. This pre-trained model allows rapid inference on real hand images without requiring complex real-time scene analysis, reducing computational intensity while preserving lighting estimation accuracy.
2Reliability
If complex scene analysis is performed for light estimation, then lighting coherence is improved, but latency increases
Solution Approach 1:
The system performs preliminary training of machine learning models on synthetic hand images with known lighting conditions before runtime. This pre-computed knowledge enables rapid light estimation during actual XR usage, eliminating the need for complex real-time scene analysis and significantly reducing latency while maintaining lighting coherence.
Solution Approach 2:
The patent transforms the light estimation problem from analyzing complex scene parameters to estimating lighting from simplified hand image parameters. By changing the input parameters from full scene data to focused hand region data, the system achieves faster processing with maintained lighting coherence.
3Productivity
If hand-based light estimation is implemented, then computational efficiency is improved, but adaptability to different hand gestures decreases
Solution Approach 1:
The system segments the hand into multiple regions of interest and applies different processing strategies to each segment. This allows the model to focus computational resources on gesture-critical regions while maintaining overall computational efficiency, resolving the contradiction between productivity and adaptability.
Solution Approach 2:
The patent implements dynamic model selection where different machine learning models are applied based on the detected hand gesture type. This dynamic adaptation allows the system to maintain high computational efficiency for common gestures while providing enhanced accuracy for complex gestures, balancing productivity and adaptability.
Data Source
AI summary
Examples described herein relate to hand-based light estimation for extended reality (XR). An image sensor of an XR device is used to obtain an image of a hand in a real-world environment. At least part of the image is processed to detect a pose of the hand. One of a plurality of machine learning models is selected based on the detected pose. At least part of the image is processed via the machine learning model to obtain estimated illumination parameter values associated with the hand. The estimated illumination parameter values are used to render virtual content to be presented by the XR device.