Panorama Panel Representation for Distortion Reduction
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Solution Overview
Problem
Existing computer vision techniques face challenges in processing panorama images due to panoramic distortions, particularly in maintaining geometric continuity and accuracy when generating depth maps, semantic maps, and layouts, especially in indoor environments.
Innovation Solution
The use of a panel geometry embedding network and a local-to-global transformer network to encode and process the geometric features of panorama images, reducing distortion and enhancing continuity by partitioning the images into panels and aggregating local and global context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If panorama images are processed using traditional computer vision techniques, then processing speed is maintained, but geometric continuity and accuracy deteriorate due to panoramic distortions
Solution Approach 1:
The panorama image is divided into multiple overlapping local patches, allowing traditional computer vision techniques to process each patch independently while maintaining geometric accuracy. This segmentation approach resolves the contradiction by breaking down the complex distorted panorama into manageable local regions where geometric continuity can be preserved without requiring complex global processing.
Solution Approach 2:
A neural network-based distortion correction module serves as an intermediary between the input panorama and processing algorithms. This intermediary corrects panoramic distortions before processing, enabling traditional techniques to maintain geometric continuity without dealing with the complexity of distorted geometry directly.
2Measurement precision
If panorama images are partitioned into panels and processed with neural networks, then geometric accuracy improves, but computational complexity increases
Solution Approach 1:
The panorama is partitioned into overlapping local patches that are processed independently by neural networks. This segmentation allows parallel processing of multiple regions, improving depth estimation accuracy through localized geometric understanding while managing computational energy by avoiding global processing of the entire high-resolution panorama at once.
Solution Approach 2:
The method processes multiple overlapping patches with some regions being processed more than once. This partial redundancy improves measurement precision at boundaries between patches while the overlapping strategy allows efficient computation by reusing features from adjacent regions, balancing accuracy improvements with computational energy consumption.
3Productivity
If traditional processing methods are used on panorama images, then computational efficiency is maintained, but distortion effects worsen in generated maps
Solution Approach 1:
By dividing the panorama into local patches, the method maintains processing efficiency through parallel computation on smaller regions while significantly reducing panoramic distortion effects. Each local patch experiences minimal distortion, allowing traditional efficient processing methods to work effectively on corrected local geometries.
Solution Approach 2:
The neural network-based distortion correction acts as an intermediary processing step that eliminates harmful panoramic distortions before main processing occurs. This intermediary step preserves productivity by using efficient neural network operations while removing the distortion that would otherwise degrade the quality of generated depth maps, semantic maps, and layouts.
Data Source
AI summary
This disclosure relates generally to image processing and specifically to processing panorama images using neural networks to generate depth maps, layouts, semantic maps or the like with reduced distortion and improved continuity. Methods and systems are described for generating such maps by leveraging several essential properties of these panorama images and by using a panorama panel representation and a neural network framework. A panel geometry embedding network is incorporated for encoding both the local and global geometric features of the panels in order to reduce negative impact of panoramic distortion. A local-to-global transformer network is also incorporated for capturing geometric context and aggregating local information within a panel and panel-wise global context.