Neural Radiance Field View Synthesis for Visual Localization Training
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Solution Overview
Problem
Training neural networks for visual localization is computationally expensive and time-consuming, especially when multiple networks are required for different scenes, due to the difficulty and cost of obtaining vast amounts of training data comprising camera poses and images.
Innovation Solution
Utilizing a neural radiance field to generate training images efficiently by synthesizing color and depth images from a variety of viewpoints, and employing uncertainty maps to mitigate artifacts and optimize the training process.
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 intensity increase
Solution Approach 1:
The patent creates virtual copies of real training data by using a neural radiance field to synthesize images from arbitrary viewpoints. Instead of collecting more real images requiring additional physical data collection, the system generates synthetic training examples by rendering views of the scene from the neural radiance field, effectively copying the essential visual information in a computationally efficient manner.
Solution Approach 2:
The patent replaces the mechanical process of physical data collection (deploying cameras, traveling to locations, capturing images) with a computational rendering process. The neural radiance field, once trained on a limited set of real images, can generate unlimited training views through software rendering, substituting physical data acquisition with virtual synthesis.
2Reliability
If vast amounts of training data are collected to train multiple neural networks for different scenes, then model performance and generalization are improved, but data collection time and cost increase
Solution Approach 1:
The patent creates a universal training data generation system where a single neural radiance field, trained on a small set of real images from a scene, can generate training data for multiple different neural network models and various viewpoints. This universal renderer replaces the need to collect separate datasets for each model or viewpoint, providing multi-functional training data generation from a single scene understanding.
Solution Approach 2:
The patent performs preliminary action by first training the neural radiance field on a small set of real images to learn the scene representation. Once this preliminary scene understanding is established, the system can rapidly generate unlimited training views without returning to physical data collection, performing the time-consuming learning phase once and reaping benefits for all subsequent training needs.
3Measurement precision
If traditional methods are used to collect training data with camera poses and images, then data accuracy is improved, but the difficulty and cost of data collection increase
Solution Approach 1:
The patent introduces the neural radiance field as an intermediary between real world scenes and training data. Instead of directly collecting images with associated pose information from the physical world, the system uses the neural radiance field as a mediator that has learned the scene geometry and appearance, then generates synthetic images with known poses. This intermediary preserves accuracy while simplifying the data collection process.
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.