Cross-View Image Labeling With Camera Pose Overlays
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Determining feature correspondences between images from different perspectives, such as top-down and ground-level views, is technically challenging due to the large amount of data to be processed and the difficulty in identifying common features across varying scales and orientations, leading to increased error and time consumption in manual labeling.
Innovation Solution
A system that generates meta data indicating the positional and orientational relationship between images from different perspectives, allowing for dynamic overlays in a user interface to facilitate accurate feature correspondence by providing contextual data and highlighting corresponding positions, thereby simplifying the labeling process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used to identify feature correspondences across different image views, then feature alignment can be achieved, but the process consumes excessive time and is prone to errors due to the large amount of data and varying scales
Solution Approach 1:
The system performs preliminary processing of camera pose data and camera trajectory data to generate metadata indicating the position and orientation of the first perspective view relative to the second perspective view before the labeling process begins. This pre-computed spatial relationship information is then used to automatically align features across different image views, eliminating the need for time-consuming manual trial-and-error alignment while maintaining high accuracy.
2Reliability
If manual labeling without contextual guidance is used, then feature identification can be performed, but error rates increase due to difficulty in identifying common features across varying scales and orientations
Solution Approach 1:
The system introduces metadata as an intermediary element that mediates between the first perspective view image and the second perspective view image. This metadata, derived from camera pose and trajectory data, provides contextual spatial information that guides the labeling process by indicating the relative position and orientation between views, thereby reducing labeling errors without significantly increasing process complexity.
3Measurement precision
If comprehensive camera pose and trajectory data processing is performed, then accurate cross-view alignment can be achieved, but data processing complexity and computational requirements increase
Solution Approach 1:
The system extracts only the essential spatial relationship information from the comprehensive camera pose and trajectory data to generate metadata indicating the position and orientation of the first perspective view relative to the second perspective view. This extraction approach maintains high alignment accuracy by preserving critical geometric relationships while reducing data processing complexity by discarding redundant information.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
An approach is provided for image labeling for cross view alignment. The approach, for example, involves determining camera pose data, camera trajectory data, or a combination thereof for a first image depicting an area from a first perspective view. The approach also involves processing the camera pose data, the camera trajectory data, or a combination thereof to generate meta data indicating a position, an orientation, or a combination thereof of the first perspective view of the area relative to a second image depicting the area from a second perspective view. The approach further involves providing data for presenting the meta data in a user interface as an overlay on the second perspective view.