Neural Radiance Field Image Generation for Faster Visual Localization Training
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Solution Overview
Problem
Training neural networks for visual localization is computationally expensive and time-consuming, particularly when many networks are required for different scenes, and existing methods are energy and carbon intensive.
Innovation Solution
Utilize a neural radiance field to generate training images efficiently by training with a limited set of real data, computing images from various viewpoints, and employing uncertainty-guided selection to mitigate artifacts, using color and depth uncertainty maps to filter out less informative images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If more computing resources are made available to carry out neural network training, then training performance and speed are improved, but energy consumption and carbon emissions increase
Solution Approach 1:
The patent uses a neural radiance field to generate synthetic training images as copies of real scene data. Instead of collecting and processing large amounts of real training images from multiple viewpoints, the system creates synthetic copies through neural rendering, significantly reducing the computational resources and energy required for training while maintaining training effectiveness
Solution Approach 2:
The patent performs preliminary action by pre-training a neural radiance field on a limited set of real images before generating training data. This preliminary modeling of the scene's radiance field enables subsequent rapid generation of training images without requiring extensive computational resources during the actual neural network training phase
2Reliability
If a large number of training examples are used to train neural networks for different scenes, then training accuracy is improved, but training time and computational cost rapidly increase
Solution Approach 1:
The system generates synthetic training examples by rendering images from multiple viewpoints using the trained neural radiance field. These synthetic copies provide diverse training data for different virtual camera positions and angles without requiring physical collection of numerous real images, thus improving training accuracy while avoiding the time and computational costs of acquiring large datasets
Solution Approach 2:
The neural radiance field serves multiple functions: it acts as both the training data generator and the scene representation model. Once trained on a small dataset, the same neural radiance field can generate training images for multiple different neural networks targeting the same scene, eliminating the need to collect separate large datasets for each training task
3Productivity
If training data is generated from limited real data using neural radiance fields, then data efficiency is improved, but image quality artifacts may increase
Solution Approach 1:
The patent extracts only the essential scene geometry and radiance information needed for training by using a neural radiance field to represent the scene. This extraction approach generates sufficient training data with acceptable quality by focusing on capturing the critical visual features rather than perfectly reproducing all scene details, thereby maintaining data generation efficiency while managing image quality
Data Source
AI summary
A plurality of training examples is accessed, each training example comprising an image of a scene and a pose of a viewpoint from which the image was captured. A neural radiance field is trained using the training examples. A plurality of generated images is computed, by, for each of a plurality of randomly selected viewpoints, generating a color image and a depth image of the scene from the neural radiance field. A neural network is trained using the generated images.