Panoramic Image Pose Estimation via Virtual Camera Segmentation
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Solution Overview
Problem
Existing technologies face challenges in aligning 360° panoramic images with 3D models of scenes, particularly in minimizing alignment errors and integrating 360 images into SfM models.
Innovation Solution
A computer-implemented method that involves obtaining a 3D representation of a scene from perspective images, converting panoramic images into second perspective images associated with virtual cameras, estimating 6D poses of virtual cameras, selecting poses that meet predefined criteria, and estimating the pose of the panoramic image within the 3D representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If panoramic images are directly aligned with 3D models using conventional methods, then alignment speed is improved, but alignment precision deteriorates due to inherent alignment errors
Solution Approach 1:
The panoramic image is segmented into multiple overlapping perspective image patches, each of which can be independently localized and matched with the 3D model. This segmentation allows the system to process smaller, more manageable regions with higher precision while maintaining overall alignment speed through parallel processing of multiple patches.
Solution Approach 2:
Perspective image patches serve as an intermediary representation between the panoramic image and the 3D model. These patches are extracted from the panoramic image using a virtual camera model, and each patch is independently matched with corresponding regions in the 3D model, thereby reducing direct alignment errors while maintaining processing efficiency.
2Measurement precision
If panoramic images are converted into multiple perspective images for localization, then alignment precision is improved, but device complexity increases
Solution Approach 1:
The system employs a universal virtual camera model that can generate multiple perspective views from a single panoramic image. This multi-functional approach allows the same panoramic image to be processed into various perspective patches depending on the virtual camera parameters, providing flexibility without requiring multiple physical cameras or complex processing pipelines for each view.
Solution Approach 2:
The system changes parameters of the virtual camera model (such as focal length, field of view, and camera position) to generate different perspective patches from the same panoramic image. By adjusting these parameters, the system can create multiple views with different characteristics, enabling precise localization while keeping the base processing pipeline relatively simple and reusable.
3Productivity
If all extracted perspective images are used for pose estimation, then processing completeness is improved, but reliability deteriorates due to outlier poses
Solution Approach 1:
The system implements a feedback mechanism where estimated poses from individual perspective patches are evaluated against predefined criteria. Patches that produce outlier poses or fail to meet quality thresholds are identified and excluded from the final pose estimation. This feedback loop ensures that only reliable, high-quality patches contribute to the final result, improving overall reliability while maintaining processing completeness through systematic validation.
Solution Approach 2:
The system applies different quality standards and validation criteria to different perspective patches based on their local characteristics. Instead of treating all patches uniformly, the system evaluates each patch's contribution to the final pose estimation individually, allowing high-quality patches to have greater influence while filtering out problematic regions. This local quality assessment improves reliability by ensuring that only trustworthy patches affect the final result.
Data Source
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AI summary
The invention relates to a method for estimating a pose of a panoramic image, comprising: obtaining a 3D representation of a scene based on a set of first perspective images of the scene using a reconstruction module; providing the panoramic image made by a panoramic camera in the scene; converting the panoramic image into a set of second perspective images of the scene, each second perspective image being associated with a virtual camera; for each second perspective image, estimate a 6D pose of the virtual camera in the 3D representation based on the second perspective image using a localization module; selecting estimated 6D poses which fulfill a predefined set of criteria; and estimating the pose of the panoramic image in the 3D representation based on the second perspective images associated with the selected 6D poses using the localization module.