NeRF Grid Processing With Shifted-Window Transformers for Efficient 3D Vision
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Solution Overview
Problem
Conventional computer vision techniques, such as convolutional neural networks (CNNs) and vision transformers (ViTs), face challenges in efficiently performing three-dimensional (3D) computer vision tasks due to high computational demands, particularly when operating on images that do not provide a full representation of the scene.
Innovation Solution
The use of a neural radiance field (NeRF) network to generate a 3D representation from two-dimensional (2D) images, combined with a modified shifted window visual transformer (SWIN ViT) architecture, allows for the production of 3D patches and feature maps, enabling efficient training and performance of 3D computer vision tasks for cyber-physical systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional computer vision techniques (CNNs and ViTs) are used for 3D tasks, then the system can process images, but the computational demands become excessively high
Solution Approach 1:
The patent transforms 2D image data into a 3D neural radiance field representation, adding the spatial dimension of depth. This allows the system to perform 3D computer vision tasks by processing the 3D NeRF representation rather than processing multiple 2D images through conventional methods, thereby reducing computational complexity while maintaining 3D understanding capabilities
Solution Approach 2:
The system creates a synthetic 3D radiance field representation (a computational model) of the scene from 2D images. This NeRF representation serves as a compressed and efficient copy of the 3D scene that can be processed with lower computational cost compared to analyzing multiple 2D images and their relationships, enabling efficient 3D tasks like depth estimation and scene understanding
2Loss of information
If 2D images are used to represent a scene, then the data collection is simple, but the representation does not provide full scene information needed for 3D tasks
Solution Approach 1:
The system adds the depth dimension to 2D image data by constructing a 3D neural radiance field representation. This NeRF representation encodes both the 2D visual appearance and the 3D spatial structure of the scene, preserving complete scene information including depth, geometry, and lighting effects without requiring complex multi-camera setups or additional sensors
Solution Approach 2:
The system performs a preliminary representation learning step where it constructs the 3D NeRF representation from 2D images before performing downstream 3D computer vision tasks. This pre-computed 3D representation contains all necessary spatial information and can be efficiently processed for various tasks without needing to reprocess the original 2D images or use complex multi-view geometry algorithms
Data Source
AI summary
A system for performing a three-dimensional computer vision task using a neural radiance field grid representation of a scene produced from two-dimensional images of at least a portion of the scene can include a processor and a memory. A neural radiance field grid network module can produce, from the two-dimensional images, three-dimensional patches of the neural radiance field grid representation of the scene. A three-dimensional shifted window visual transformer module can produce, from the three-dimensional patches, a feature map. A first decoder module can produce, from the feature map, the neural radiance field grid representation of the scene to train the system. A second decoder module can produce, from the feature map, the neural radiance field grid representation of the scene to perform the three-dimensional computer vision task for a cyber-physical system.