Single-Image Panorama Generation with Neural Networks for VR
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Solution Overview
Problem
Users face difficulties in generating panoramic images from two-dimensional images captured by conventional cameras, as existing methods do not allow for the creation of panoramic content suitable for virtual reality applications from a single image.
Innovation Solution
A system utilizing generative neural networks, such as GANs and VAEs, processes a single image to generate a panoramic image by extracting features, generating intermediate representations like a cubemap, and transforming them into equirectangular format, incorporating metadata for compatibility with VR platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single 2D image is used as input, then the ease of operation is improved, but the quality and completeness of the panoramic output deteriorates
Solution Approach 1:
The patent introduces an intermediate cubemap representation as a mediator between the 2D input image and the equirectangular panoramic output. The neural network first generates a cubemap from the 2D image, then transforms it to equirectangular format. This intermediary step allows the system to handle the geometric transformation more effectively, preserving panoramic quality while accepting simple 2D inputs.
Solution Approach 2:
The patent replaces traditional multi-camera mechanical systems or complex multi-image stitching processes with a neural network-based image generation system. Instead of requiring multiple physical cameras or manual image alignment, a single neural network model processes one 2D image to generate the complete panoramic view, substituting mechanical complexity with computational intelligence.
2Manufacturing precision
If multiple images are captured to create a panorama, then the panoramic image quality is improved, but the ease of operation deteriorates
Solution Approach 1:
The patent extracts the essential geometric and semantic features from a single 2D image using the neural network, then synthesizes the complete panoramic view from these extracted features. Instead of requiring multiple images to be captured and stitched, the system extracts sufficient information from one image and generates the remaining panoramic content computationally, eliminating the operational complexity of multi-image capture.
Solution Approach 2:
The neural network learns from training data consisting of paired 2D images and their corresponding panoramic versions, creating an internal model that copies the relationship between single views and panoramic views. During inference, the network applies this learned transformation to generate panoramic images from new 2D inputs, effectively copying the panoramic structure from training examples to new generations.
3Device complexity
If conventional camera systems are used, then the device complexity is reduced, but the ability to generate VR-compatible content deteriorates
Solution Approach 1:
The patent makes a single conventional camera system universal by adding neural network processing that enables it to perform multiple functions: capturing 2D images, generating panoramic views, creating VR-compatible content, and producing intermediate cubemap representations. The same hardware input (single camera) can now serve multiple output purposes through the intelligent processing layer, eliminating the need for specialized multi-camera VR rigs.
4Productivity
If a single image processing step is used, then the productivity is improved, but the image processing quality deteriorates
Solution Approach 1:
The patent segments the image generation process into distinct neural network stages: feature extraction from the 2D input, cubemap generation, and equirectangular transformation. Each stage is handled by specialized network components that can be independently optimized. This segmentation allows the system to maintain high processing speed through automated one-step input while achieving high quality through specialized processing at each stage.
Data Source
AI summary
Apparatuses, systems, and techniques are presented to generate panoramas from individual images. In at least one embodiment, one or more generative neural networks are used to generate a spherical panoramic image using features extracted from a single input image.