Samsung electronics co., ltd. (20240303789). REFERENCE-BASED NERF INPAINTING simplified abstract
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 20240303789 titled 'REFERENCE-BASED NERF INPAINTING
Abstract: The method described in this patent application involves training a neural radiance field to render a 3D scene from a new viewpoint with view-dependent effects. The training process includes multiple loss functions associated with different aspects of the reference and target images.
- Neural radiance field trained using first loss on unmasked regions of reference and target images
- Training updated with second loss based on depth estimate of masked region in reference image
- Further training with third loss on view-substituted image from reference viewpoint with substituted target colors
- Optional fourth loss associated with dis-occluded pixels in target image
Potential Applications: - Virtual reality and augmented reality applications - Gaming industry for realistic rendering of scenes - Architectural visualization and design
Problems Solved: - Generating realistic renderings from novel viewpoints - Capturing view-dependent effects in 3D scenes - Improving the training process for neural radiance fields
Benefits: - Enhanced visual quality in rendered images - More accurate depth estimation in 3D scenes - Improved realism in virtual environments
Commercial Applications: Title: Advanced Rendering Technology for Virtual Environments This technology can be utilized in industries such as virtual reality, gaming, and architectural design to create immersive and realistic visual experiences. The market implications include enhanced user engagement, improved design visualization, and increased demand for high-quality rendering solutions.
Prior Art: Prior research in neural rendering techniques, volumetric rendering, and view synthesis methods can provide valuable insights into the development of this technology.
Frequently Updated Research: Researchers are constantly exploring new approaches to improve neural rendering techniques, such as incorporating additional loss functions, optimizing network architectures, and enhancing training processes.
Questions about Neural Radiance Field Technology: 1. How does the neural radiance field differ from traditional rendering methods? The neural radiance field represents a scene as a continuous function, allowing for more flexible rendering from novel viewpoints compared to traditional discrete rendering techniques.
2. What are the key challenges in training a neural radiance field for rendering 3D scenes? Training a neural radiance field requires balancing multiple loss functions to capture different aspects of the scene, such as color consistency, depth estimation, and view-dependent effects.
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.
