NeRF Patch Training for Sparse-View Novel View Synthesis
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Solution Overview
Problem
Neural Radiance Fields (NeRF) models perform poorly with sparse input data, limiting their application in areas like AR/VR and robotics where dense input data is challenging to obtain.
Innovation Solution
Training NeRF models using image patches and a flow model, utilizing ground truth patches and a loss function to adjust parameters, and incorporating a discriminator model for improved performance with sparse data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If NeRF models are trained with sparse input data, then the model can be applied to AR/VR and robotics scenarios, but the rendering performance and accuracy drop significantly
Solution Approach 1:
The training images are segmented into multiple patches, and the NeRF model is trained to render corresponding patches from novel views. This segmentation approach allows the model to learn from sparse input data more effectively by focusing on local regions, thereby maintaining rendering accuracy while adapting to sparse data scenarios.
Solution Approach 2:
Different parts of the scene are treated with different quality requirements. The model uses patch-based rendering where each patch can be optimized independently, allowing high-quality rendering in important regions while tolerating lower quality in less critical areas, thus maintaining overall accuracy with sparse inputs.
2Manufacturing precision
If multiple dense input images are collected to improve NeRF performance, then rendering quality improves, but data collection becomes infeasible for many applications
Solution Approach 1:
Instead of requiring complete dense coverage of the entire scene, the patent uses patch-based rendering where only partial views of each patch are needed from the sparse input images. This partial action approach allows the model to achieve good rendering quality without requiring excessive input data that would be infeasible to collect in many applications.
3Quantity of substance
If the NeRF model is trained to handle sparse inputs, then data requirements are reduced, but the model loses the ability to produce photorealistic renderings
Solution Approach 1:
By segmenting the rendering task into patches, the model can process and render each patch with sufficient detail to maintain photorealistic quality, even when trained on sparse input data. The segmentation allows the model to allocate its limited learned information more effectively across the scene.
Solution Approach 2:
The patent introduces an image prior model as an intermediary that provides additional constraints and guidance during patch-based rendering. This intermediary helps bridge the gap between sparse input data and photorealistic output by incorporating general knowledge about what natural images should look like.
Data Source
AI summary
Systems and methods for training a neural radiance field model can include the use of image patches for ground truth training. For example, the systems and methods can include generating patch renderings with a neural radiance field model, comparing the patch renderings to ground truth patches from ground truth images, and adjusting one or more parameters based on the comparison. Additionally and/or alternatively, the systems and methods can include the utilization of a flow model for mitigating and/or minimizing artifact generation.