Albedo Texture Neural Rendering for Fast 3D Novel View Synthesis
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Solution Overview
Problem
Neural radiance field (NeRF) networks consume excessive resources and operate inefficiently, leading to delayed rendering times and quality degradation in 3D scene generation, especially on resource-constrained devices like mobile devices.
Innovation Solution
Utilize a trained set of albedo textures and a machine learning model to generate novel viewpoints of real-world objects by capturing and processing a set of training images, reducing resource consumption and improving rendering efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If NeRF networks are used for 3D scene generation, then photorealistic quality is achieved, but resource consumption increases and rendering time delays
Solution Approach 1:
The patent segments the complex NeRF network into separate components: a 3D mesh model and independent texture maps. This segmentation allows each component to be processed and stored separately, reducing the computational burden during rendering while maintaining photorealistic quality through the combination of geometric accuracy and detailed textures.
Solution Approach 2:
The patent creates simplified copies of the scene geometry through 3D mesh models that approximate the original scenes. These mesh copies, combined with texture maps, serve as lightweight representations that consume fewer resources during rendering compared to the full NeRF networks, while still achieving photorealistic results.
2Manufacturing precision
If NeRF networks are used for 3D scene generation, then photorealistic quality is achieved, but rendering time increases
Solution Approach 1:
By segmenting the rendering process into mesh generation and texture application stages, the patent enables faster rendering. The 3D mesh provides efficient geometric representation for rapid rendering, while pre-computed texture maps are applied without requiring complex neural network inference during the actual rendering phase, significantly reducing rendering time.
Solution Approach 2:
The patent performs preliminary processing to generate 3D mesh models and compute texture maps during the training phase. This preliminary action prepares all necessary components in advance, so that during actual rendering, the system only needs to assemble and render these pre-computed elements, dramatically reducing rendering time while preserving photorealistic quality.
3Adaptability or versatility
If NeRF networks are used on mobile devices, then 3D view generation is enabled, but device performance degrades
Solution Approach 1:
The patent creates simplified mesh-based copies of complex scenes that are much lighter than full NeRF representations. These mesh copies with texture maps can be stored in mobile device memory and rendered using standard graphics hardware, enabling 3D view generation on mobile devices without the excessive resource consumption of NeRF networks.
Solution Approach 2:
The patent uses computationally inexpensive mesh models and texture maps instead of expensive NeRF networks. These simplified representations can be quickly rendered and discarded for different viewpoints, allowing mobile devices to generate multiple 3D views without sustained high performance requirements, thus protecting device performance.
Data Source
AI summary
Methods and systems are disclosed for performing operations for generating a photorealistic rendering of an object. The operations include: accessing a set of albedo textures and a machine learning model associated with a real-world object, the set of albedo textures and a machine learning model having been trained based on a plurality of viewpoints of the real-world object; obtaining a three-dimensional (3D) mesh of the real-world object; receiving input that selects a new viewpoint that differs from the plurality of viewpoints of the real-world object; and generating a photorealistic rendering of the real-world object from the new viewpoint based on the 3D mesh of the real-world object, the set of albedo textures, and the machine learning model associated with the real-world object.