18185230. Fast Large-Scale Radiance Field Reconstruction simplified abstract (Adobe Inc.)
Contents
- 1 Fast Large-Scale Radiance Field Reconstruction
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Fast Large-Scale Radiance Field Reconstruction - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Radiance Field Reconstruction
- 1.13 Original Abstract Submitted
Fast Large-Scale Radiance Field Reconstruction
Organization Name
Inventor(s)
Zexiang Xu of San Jose CA (US)
Xiaoshuai Zhang of San Diego CA (US)
Kalyan Sunkavalli of San Jose CA (US)
Fast Large-Scale Radiance Field Reconstruction - A simplified explanation of the abstract
This abstract first appeared for US patent application 18185230 titled 'Fast Large-Scale Radiance Field Reconstruction
Simplified Explanation
The patent application describes a method for fast large-scale radiance field reconstruction using machine learning models to generate local and global volumes from input images of a scene, synthesizing a novel view of the scene.
- Image features are extracted from a sequence of input images using an image encoder.
- Machine learning models generate a local volume based on the image features of one or more images.
- Another set of machine learning models generate a global volume based on the local volume.
- A new view of the scene is created using the global volume.
Key Features and Innovation
- Utilizes machine learning models for large-scale radiance field reconstruction.
- Synthesizes a novel view of a scene based on input images.
- Efficiently generates local and global volumes to reconstruct the radiance field.
Potential Applications
- Virtual reality and augmented reality applications.
- 3D scene reconstruction for gaming and entertainment industries.
- Architectural visualization and design.
- Medical imaging for volumetric reconstruction.
- Environmental simulations and modeling.
Problems Solved
- Speeds up the process of radiance field reconstruction.
- Enables the creation of realistic and immersive virtual environments.
- Facilitates accurate 3D scene reconstruction from 2D images.
Benefits
- Enhanced visual quality in virtual and augmented reality experiences.
- Improved efficiency in generating novel views of scenes.
- Enables realistic simulations for various industries.
- Facilitates accurate volumetric reconstruction for medical applications.
Commercial Applications
- Virtual reality content creation tools.
- Gaming and entertainment software development.
- Architectural and interior design software.
- Medical imaging software for volumetric reconstruction.
- Environmental modeling and simulation tools.
Prior Art
There may be prior art related to radiance field reconstruction techniques using machine learning models and image features extraction. Researchers and practitioners in the fields of computer vision, graphics, and machine learning may have explored similar methods.
Frequently Updated Research
Researchers are constantly exploring advancements in machine learning models for image processing and scene reconstruction. Stay updated on conferences and journals in the fields of computer vision, graphics, and artificial intelligence for the latest research in radiance field reconstruction.
Questions about Radiance Field Reconstruction
How does radiance field reconstruction using machine learning models differ from traditional methods?
Radiance field reconstruction using machine learning models offers a more efficient and accurate way to reconstruct scenes compared to traditional methods. By leveraging the power of machine learning, the process can be automated and optimized for large-scale reconstructions.
What are the potential challenges in implementing radiance field reconstruction using machine learning models?
Implementing radiance field reconstruction using machine learning models may face challenges such as data preprocessing, model training, and optimization for real-time applications. Ensuring the accuracy and reliability of the reconstructed scenes will be crucial in practical implementations.
Original Abstract Submitted
Embodiments are disclosed for fast large-scale radiance field reconstruction. A method of fast large-scale radiance field reconstruction may include receiving a sequence of input images that depict views of a scene and extracting, using an image encoder, image features from the sequence of input images. A first one or more machine learning models may generate a local volume based on the image features corresponding to one or more images from the sequence of input images. A second one or more machine learning models may generate a global volume based on the local volume. A novel view of the scene is synthesized based on the global volume.
