Neural Radiance Field Inpainting for 3D Scene Editing
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Solution Overview
Problem
Existing technologies face challenges in maintaining consistency and realism when modifying three-dimensional (3D) representations of environments, such as removing objects like vehicles or pedestrians from neural radiance field (NeRF) representations.
Innovation Solution
The use of diffusion models, specifically generative diffusion models, to update or supplement NeRF representations by inpainting regions in 3D environments, allowing for the removal and replacement of objects with coherent and realistic background content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional methods are used to modify 3D representations, then object removal can be achieved, but consistency and realism throughout the 3D representation deteriorate
Solution Approach 1:
The patent introduces an inpainting model as an intermediary between the NeRF representation and the final modified output. This inpainting model generates intermediate representations that guide the modification process, ensuring that removed objects are replaced with coherent background content that maintains visual consistency across all views of the 3D environment
Solution Approach 2:
The patent modifies parameters of the NeRF representation by adjusting weights and biases of the neural network. By changing these parameters iteratively during the inpainting process, the system can remove objects while maintaining the overall structural integrity and realism of the 3D representation across multiple views
2Reliability
If generative models are used to replace removed content, then realism of replacement content improves, but computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary actions by first identifying and masking the regions containing objects to be removed before generating replacement content. The inpainting model is prepared and configured in advance with the masked reference view, allowing it to efficiently generate realistic replacement content only for the necessary regions rather than processing the entire 3D representation
Solution Approach 2:
The patent applies local quality by focusing computational resources on specific regions of the 3D representation where objects need to be removed. The inpainting model generates replacement content locally for masked regions while preserving the original content in unmasked regions, reducing overall computational complexity while maintaining realism where it matters most
Data Source
AI summary
In various examples, systems and methods are disclosed relating to neural networks for three-dimensional (3D) scene representations and modifying the 3D scene representations. In some implementations, a diffusion model can be configured to modify selected portions of 3D scenes represented using neural radiance fields, without painting back in content of the selected portions that was originally present. A first view of the neural radiance fields can be inpainted to remove a target feature from the first view, and used as guidance for updating the neural radiance field so that the target feature can be realistically removed from various second views of the neural radiance fields while context is retained outside of the selected portions.