Lighting Estimation Model for HDR Prediction
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Solution Overview
Problem
Augmented Reality (AR) systems face challenges in matching lighting conditions when inserting synthetic objects into real scenes, as existing methods struggle to accurately predict high-dynamic range (HDR) lighting from low-dynamic range (LDR) background images.
Innovation Solution
A method is developed to predict HDR lighting by generating a lighting estimation model using LDR background images captured with reference objects of different reflectance properties, employing machine learning techniques to train a model that can infer HDR lighting from LDR images, allowing for realistic rendering of virtual objects in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional HDR imaging methods are used to capture lighting information, then lighting accuracy is improved, but device complexity and capture time increase significantly
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between LDR image capture and HDR lighting estimation. Instead of using complex HDR imaging hardware or multi-exposure capture systems, the system uses a trained neural network model that takes simple LDR images as input and predicts HDR lighting conditions. This intermediary model resolves the contradiction by providing accurate lighting estimation without requiring complex imaging devices or capture procedures.
Solution Approach 2:
The patent replaces mechanical/optical HDR imaging systems with a computational approach using machine learning. Rather than using multiple cameras, filters, or exposure settings to capture HDR data, the system substitutes these physical mechanisms with a trained neural network that processes standard LDR images to infer HDR lighting information, thereby simplifying the device requirements while maintaining lighting accuracy.
2Measurement precision
If multi-exposure HDR capture is performed to achieve accurate lighting, then lighting precision is improved, but frame rate and real-time performance deteriorate
Solution Approach 1:
The patent applies preliminary action by pre-training a machine learning model offline using large datasets of LDR-HDR image pairs. During runtime, the pre-trained model can rapidly process single LDR frames to predict HDR lighting without requiring multiple exposures or complex capture sequences. This preliminary preparation resolves the contradiction by enabling real-time performance while maintaining lighting precision through the use of the pre-learned model.
Solution Approach 2:
The patent substitutes the mechanical process of multi-exposure capture with a computational prediction process. Instead of physically capturing multiple exposures at different time points (which reduces frame rate), the system uses a machine learning model to instantly predict HDR lighting from a single LDR frame, thereby maintaining both precision and real-time performance.
3Productivity
If simple LDR image capture is used without HDR processing, then device complexity and processing time are reduced, but lighting accuracy and rendering realism deteriorate
Solution Approach 1:
The patent applies parameter changes by transforming the output of a standard LDR capture system into HDR lighting parameters through machine learning. The model learns to predict HDR lighting parameters (such as irradiance, reflectance, and environmental maps) from LDR image data, effectively changing the parameter representation from low-dynamic-range to high-dynamic-range while maintaining processing efficiency. This resolves the contradiction by achieving lighting accuracy through parameter transformation rather than through complex capture hardware.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary that bridges simple LDR capture and accurate HDR lighting representation. The model processes the LDR image data and generates HDR lighting estimates, serving as a computational mediator that enhances lighting accuracy without requiring the input system to be complex. This intermediary approach maintains processing speed while improving lighting precision.
4Measurement precision
If reference objects with different reflectance properties are captured to train the model, then lighting estimation accuracy is improved, but data collection complexity increases
Solution Approach 1:
The patent applies universality by designing a training dataset collection process that uses a single camera to capture multiple types of reference objects with different reflectance properties (matte, glossy, metallic). This multi-functional approach allows the system to gather diverse reflectance data using one device, thereby improving lighting estimation accuracy across different material types without proportionally increasing device complexity. The same imaging system serves multiple purposes by capturing various material responses to lighting.
Data Source
AI summary
An example method, apparatus, and computer-readable storage medium are provided to predict high-dynamic range (HDR) lighting from low-dynamic range (LDR) background images. In an example implementation, a method may include receiving low-dynamic range (LDR) background images of scenes, each LDR background image captured with appearance of one or more reference objects with different reflectance properties; and training a lighting estimation model based at least on the received LDR background images to predict high-dynamic range (HDR) lighting based at least on the trained model. In another example implementation, a method may include capturing a low-dynamic range (LDR) background image of a scene from an LDR video captured by a camera of the electronic computing device; predicting high-dynamic range (HDR) lighting for the image, the predicting, using a trained model, based at least on the LDR background image; and rendering a virtual object based at least on the predicted HDR lighting.