NeRF Radiance Field Learning with Outside-Region Compensation
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Solution Overview
Problem
Existing image processing techniques struggle to accurately estimate radiance fields within a limited learning region, leading to inaccuracies due to the absence of color and volume density information for background and other objects outside the learning region, which results in a gap between the generated virtual viewpoint image and the captured image.
Innovation Solution
The technique involves generating an outside-learning-region image that includes only the representation of objects outside the learning region, using a virtual background color, and estimating radiance fields by learning the color and volume density within the limited region, employing a function to determine RGB values based on pixel positions and camera parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the learning region is limited to a narrow space including only the main object, then the processing time and memory capacity required for NeRF learning are reduced, but the accuracy of the virtual viewpoint image is reduced because color and volume density information of background and other objects are missing
Solution Approach 1:
The patent segments the three-dimensional space into a learning region (narrow space including the main object) and an outside region (background and other objects). By separating these regions, the system can limit NeRF learning to only the learning region, reducing processing time and memory requirements while maintaining accuracy through separate handling of background information.
Solution Approach 2:
The patent extracts background and other object information from the captured image and stores it separately in a storage unit. This extracted information is then referenced during virtual viewpoint image generation to compensate for the limited learning region, ensuring that color and volume density information for background objects is preserved without increasing NeRF learning complexity.
2Device complexity
If the learning region is limited to a narrow space, then the device complexity is reduced, but information loss occurs because color and volume density data for objects outside the learning region are no longer available
Solution Approach 1:
The patent introduces a storage unit as an intermediary component that stores background and other object information extracted from captured images. This storage unit acts as a mediator between the limited learning region and the complete scene information, providing access to color and volume density data for objects outside the learning region without increasing NeRF system complexity.
3Measurement precision
If the number of parameters and rays for NeRF learning are increased to improve virtual viewpoint image accuracy, then the processing time and memory capacity required for learning increase
Solution Approach 1:
The patent applies local quality by concentrating computational resources (parameters and rays) only on the learning region where the main object is located, rather than distributing them across the entire three-dimensional space including background. This localized approach maintains high accuracy for the main object while significantly reducing overall processing time and memory requirements.
Data Source
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AI summary
The information processing apparatus (200) performs based data of a plurality of captured images obtained by capturing an object existing in a predetermined image capturing space from a plurality of viewpoints, camera parameters corresponding to each of the plurality of viewpoints in a case where image capturing is performed, and information on a color relating to an outside-region object, which is the object existing outside a learning region set in a three-dimensional space corresponding to the predetermined image capturing space learning of spatial information, which is information relating to a color and volume density at each position inside the learning region and the learning of spatial information is performed based on a difference between a rendering color including an accumulated color obtained by accumulating the spatial information and a color relating to the outside-region object, and a color of the captured image (S604 to S606).