Depth-Guided Lighting Estimation With Multi-Level Neural Networks
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Solution Overview
Problem
Existing lighting estimation methods struggle to accurately determine the lighting conditions in a scene from a single image without making assumptions about the material properties of the real object, particularly for close-view images where environmental information is limited.
Innovation Solution
A method utilizing multiple levels of neural networks to estimate lighting conditions by modeling light sources as a linear combination of canonical light bases, incorporating irradiance maps, and employing multi-tree-based progressive estimation to simplify complexity and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing lighting estimation methods are used to determine lighting conditions from a single image, then the process is simple, but the accuracy is insufficient especially for close-view images with limited environmental information
Solution Approach 1:
The patent segments the lighting estimation process into multiple levels of neural networks. A first neural network performs coarse lighting estimation, and a second neural network refines the estimation. This multi-level segmentation improves accuracy by breaking down the complex estimation task into manageable stages, each focusing on specific aspects of lighting conditions.
Solution Approach 2:
The patent introduces a hierarchical dimension to the lighting estimation process by using multiple levels of neural networks with different resolution requirements. The first neural network operates at a coarser level to capture overall lighting patterns, while the second neural network operates at a finer level to refine details, effectively adding a dimensional layer to the estimation architecture.
2Measurement precision
If multiple levels of neural networks are used to improve lighting estimation accuracy, then the precision improves, but the training and computation cost increases
Solution Approach 1:
The patent applies partial action by having the first neural network perform the essential coarse estimation that captures the majority of lighting characteristics. The second neural network then performs a more limited refinement task, adjusting only the necessary details. This partial action approach achieves high accuracy without requiring both networks to perform exhaustive computations at full capacity.
Solution Approach 2:
The first neural network performs preliminary lighting estimation before the second neural network refines it. This preliminary action establishes a baseline lighting condition that guides the subsequent refinement process, reducing the computational burden on the second network since it only needs to make targeted adjustments rather than perform complete estimation from scratch.
3Adaptability or versatility
If lighting estimation is performed without assumptions on material properties, then the adaptability improves, but the difficulty of detecting and measuring lighting conditions increases
Solution Approach 1:
The patent creates a universal lighting estimation system that works across different material types without requiring material-specific assumptions. The neural networks are trained to extract lighting information directly from image data, making the system adaptable to various objects and surfaces. This multi-functionality allows the same estimation pipeline to handle diverse materials while maintaining accuracy.
Solution Approach 2:
The patent replaces traditional mechanical or physics-based lighting analysis methods with neural network-based computational approaches. Instead of relying on explicit material property models and complex physical simulations, the system uses learned patterns from training data to directly estimate lighting conditions, significantly reducing the difficulty of detection while maintaining material independence.
Data Source
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AI summary
In accordance with implementations of the subject matter described herein, there is provided a solution of lighting estimation. In the solution, an input image about a real object and a depth map corresponding to the input image are obtained. A geometric structure of the scene in the input image is determined based on the depth map. Shading and shadow information on the real object caused by a light source in the scene is determined based on the determined geometric structure of the scene. Then, a lighting condition in the scene caused by the light source is determined based on the input image and the shading and shadow information. The virtual object rendered using the lighting condition obtained according to the solution can exhibit a realistic effect consistent with the real object.