Camera Rotation Calculation Using Sensor Translations in SfM
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Solution Overview
Problem
Current photogrammetry techniques fail to effectively utilize sensor-derived camera positions for early-stage camera rotation calculations in Structure-from-Motion (SfM) reconstruction, relying instead on internal camera parameters and late-stage integration due to low accuracy and lack of effective methods for determining camera rotations using pairwise information.
Innovation Solution
A software process in a photogrammetry application calculates camera rotations using translations between sensor-derived camera positions, such as GPS data, and pairwise information to produce a sensor-derived camera pose that can be integrated at an early stage of SfM reconstruction, determining optical centers and unit vectors along epipoles to estimate the best-mapping rotation matrix.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sensor-derived camera positions are used at early stages of SfM reconstruction, then calculation efficiency and processor resource consumption are improved, but measurement precision and reliability deteriorate due to low accuracy of sensor-derived positions
Solution Approach 1:
The patent applies preliminary action by calculating camera rotations using sensor-derived camera positions and pairwise information before the traditional bundle adjustment stage. The software process determines optical centers, estimates unit vectors along epipoles, and computes rotation matrices that map unit vectors from sensor-derived positions to epipolar unit vectors, producing refined camera poses early in the reconstruction pipeline.
Solution Approach 2:
The patent implements feedback by using the calculated camera rotations and sensor-derived positions to generate refined camera poses that are fed back into the SfM reconstruction process. The rotation matrices and optical centers are used to adjust and refine the 3D point cloud and camera parameter estimates, creating an iterative improvement loop that enhances overall measurement precision.
2Productivity
If sensor-derived camera positions are integrated early in SfM reconstruction, then scalability with the number of cameras is improved, but device complexity increases due to additional computational steps
Solution Approach 1:
The patent applies segmentation by dividing the camera pose calculation into distinct modular steps: (1) obtaining sensor-derived camera positions, (2) determining optical centers, (3) estimating unit vectors along epipoles from pairwise camera information, (4) calculating rotation matrices that map unit vectors, and (5) forming refined camera poses. This segmentation allows each step to be optimized independently and facilitates parallel processing.
Solution Approach 2:
The patent uses unit vectors along epipoles as an intermediary representation that bridges sensor-derived camera positions and the final camera pose. These unit vectors serve as a intermediate calculation that transforms raw sensor data into geometric constraints, enabling efficient rotation matrix computation without directly solving the complex pose estimation problem.
3Measurement precision
If traditional late-stage integration of sensor-derived camera positions is used, then measurement precision is maintained, but productivity and processing time are reduced
Solution Approach 1:
The patent performs the calculation of camera rotations and refinement of camera poses as a preliminary action before the traditional bundle adjustment stage. By computing optical centers, estimating epipolar unit vectors, and determining rotation matrices early in the pipeline, the system prepares refined camera poses that reduce computational burden in later stages and accelerate overall processing.
Data Source
AI summary
In example embodiments, techniques are provided for calculating camera rotation using translations between sensor-derived camera positions (e.g., from GPS) and pairwise information, producing a sensor-derived camera pose that may be integrated in an early stage of SfM reconstruction. A software process of a photogrammetry application may obtain metadata including sensor-derived camera positions for a plurality of cameras for a set of images and determine optical centers based thereupon. The software process may estimate unit vectors along epipoles from a given camera of the plurality of cameras to two or more other cameras. The software process then may determine a camera rotation that best maps unit vectors defined based on differences in the optical centers to the unit vectors along the epipoles. The determined camera rotation and the sensor-derived camera position form a sensor-derived camera pose that may be returned and used.