18389072. REFERENCE-BASED NERF INPAINTING simplified abstract (Samsung Electronics Co., Ltd.)
Contents
REFERENCE-BASED NERF INPAINTING
Organization Name
Inventor(s)
Ashkan Mirzaei of Toronto (CA)
Tristan TY Aumentado-armstrong of Toronto (CA)
Konstantinos G. Derpanis of Toronto (CA)
Igor Gilitschenski of Toronto (CA)
Aleksai Levinshtein of Toronto (CA)
Marcus Brubaker of Toronto (CA)
REFERENCE-BASED NERF INPAINTING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18389072 titled 'REFERENCE-BASED NERF INPAINTING
The method described in the patent application involves training a neural radiance field to render a 3D scene from a new viewpoint with view-dependent effects.
- The neural radiance field is trained using multiple losses, including one associated with unmasked regions in a reference image and target images.
- Additionally, the training involves updating the model with a loss related to a depth estimate of a masked region in the reference image.
- The model is further refined using a loss associated with a view-substituted image, which is a volume rendering from the reference viewpoint with view-substituted target colors.
- In some cases, the neural radiance field is trained with a loss related to dis-occluded pixels in a target image.
Potential Applications: - Virtual reality and augmented reality applications - Gaming industry for realistic rendering of scenes - Architectural visualization for creating lifelike representations of buildings and spaces
Problems Solved: - Generating realistic renderings of 3D scenes from novel viewpoints - Capturing view-dependent effects accurately - Improving the quality of volume renderings in computer graphics
Benefits: - Enhanced visual quality in virtual environments - Improved user experience in interactive applications - Efficient rendering of complex scenes with realistic lighting effects
Commercial Applications: Title: Advanced Rendering Technology for Virtual Environments This technology can be used in various industries such as gaming, architecture, and virtual reality to create immersive and realistic visual experiences. Companies developing virtual reality applications, video games, and architectural visualization software can benefit from incorporating this advanced rendering technology into their products.
Questions about Neural Radiance Field Technology: 1. How does the neural radiance field differ from traditional rendering techniques? The neural radiance field uses neural networks to represent scene geometry and appearance, allowing for more flexible and accurate rendering compared to traditional methods.
2. What are the key challenges in training a neural radiance field for rendering 3D scenes? Training a neural radiance field requires large amounts of data and computational resources to learn the complex relationships between scene geometry, appearance, and lighting.
Original Abstract Submitted
Provided is a method of training a neural radiance field and producing a rendering of a 3D scene from a novel viewpoint with view-dependent effects. The neural radiance field is initially trained using a first loss associated with a plurality of unmasked regions associated with a reference image and a plurality of target images. The training may also be updated using a second loss associated with a depth estimate of a masked region in the reference image. The training may also be further updated using a third loss associated with a view-substituted image associated with a respective target image. The view-substituted image is a volume rendering from the reference viewpoint across pixels with view-substituted target colors. In some embodiments, the neural radiance field is additionally trained with a fourth loss. The fourth loss is associated with dis-occluded pixels in a target image.
