NeRF Learning Time Reduction via Selective Multi-View Data
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Solution Overview
Problem
The existing techniques for generating virtual viewpoint images using Neural Radiance Fields (NeRF) require extensive learning processes, which are time-consuming due to the need for large amounts of multi-viewpoint image data.
Innovation Solution
The proposed image processing apparatus identifies the imaging apparatuses with positions or orientations closest to the virtual camera and uses only their captured image data for learning NeRF, thereby reducing the amount of data required for learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If learning of NeRF is performed repeatedly using a large amount of multi-viewpoint image data to achieve high accuracy in estimating radiance fields, then the accuracy of radiance field estimation is improved, but the learning time increases significantly
Solution Approach 1:
The patent extracts and uses only the captured image data from imaging apparatuses that are spatially close to the virtual camera position, discarding data from apparatuses far away. This selective extraction reduces the training dataset size while maintaining sufficient accuracy for radiance field estimation, thereby reducing learning time without significantly compromising measurement precision.
Solution Approach 2:
The patent applies local quality by differentiating the usefulness of captured image data based on the spatial relationship between imaging apparatuses and the virtual camera. Data from apparatuses closer to the virtual camera are prioritized for learning, as they provide more relevant information for estimating radiance fields at that specific viewpoint, while data from distant apparatuses are excluded. This localized approach optimizes the learning process by focusing computational resources on the most relevant data.
2Quantity of substance
If captured image data from all imaging apparatuses is used for learning NeRF, then the completeness of training data is improved, but the amount of data to be processed increases, leading to longer learning time
Solution Approach 1:
The patent extracts only the necessary subset of captured image data from the total available data based on spatial proximity to the virtual camera. By filtering out data from imaging apparatuses that are not spatially close, the system reduces the volume of training data to be processed while retaining the most relevant information, thus improving learning efficiency without sacrificing essential training content.
Solution Approach 2:
The patent applies partial action by using only a portion of the available captured image data—specifically, data from imaging apparatuses close to the virtual camera—rather than processing all available data. This partial usage is sufficient to achieve high accuracy in radiance field estimation for the target viewpoint, making the excessive processing of distant apparatus data unnecessary and improving overall productivity.
Data Source
AI summary
The time required for learning of NeRF is reduced. The image processing apparatus obtains image capturing parameters of each of a plurality of imaging apparatuses arranged at positions different from one another, data of a captured image obtained by image capturing by each of the plurality of imaging apparatuses, and virtual viewpoint information including at least one of information indicating a position of a virtual viewpoint and information indicating a viewing direction from the virtual viewpoint, determines a learning condition of a learning model estimating radiance fields corresponding to an object existing in an image capturing area of the plurality of imaging apparatuses based on the virtual viewpoint information, and performs learning of the learning model based on the learning condition, the image capturing parameters, and data of the captured image.