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Comparative Study of Relative-Pose Estimations from a Monocular Image Sequence in Computer Vision and Photogrammetry
Tserennadmid Tumurbaatar1, Taejung Kim2
1Department of Information and Computer Sciences, National University of Mongolia, Ulaanbaatar 14200, Mongolia. tserennadmid@seas.num.edu.mn.
Sensors (Basel, Switzerland)
|April 25, 2019
Summary
This study compares computer vision and photogrammetry for monocular pose estimation. Homography-based methods show higher accuracy than essential-matrix or relative orientation approaches, especially under noisy conditions.
Area of Science:
- Computer Vision and Photogrammetry
- Geometric Measurement and 3D Reconstruction
Background:
- Pose estimation from images is crucial for 3D interfaces, with increasing use of single cameras.
- Existing research often focuses on specific fields, lacking a direct comparison of computer vision and photogrammetry for monocular pose estimation.
- Most 3D user interfaces require additional sensors, making monocular camera-based 3D motion sensing unique.
Purpose of the Study:
- To evaluate differences between photogrammetric and computer vision techniques for object pose estimation using monocular image sequences.
- To analyze how these differences impact the choice of processing techniques for single-camera systems.
- To compare the accuracy and characteristics of different pose estimation methods under varying conditions.
Main Methods:
- Implementation of estimation methods from both computer vision and photogrammetry fields to determine 3D rotation and translation.
- Development of a mathematical motion model to differentiate geometric properties and estimation techniques.
- Precision analysis to investigate the characteristics of each method.
Main Results:
- Significant differences observed in pose estimation accuracy between computer vision and photogrammetric approaches.
- Homography-based methods demonstrated superior accuracy compared to essential-matrix or relative orientation-based methods.
- Performance variations were noted based on the characteristics of the test datasets and noise levels.
Conclusions:
- Homography-based approaches are more robust and accurate for monocular pose estimation, particularly in the presence of noise.
- The study highlights the importance of considering the specific characteristics of photogrammetric and computer vision methods when selecting a pose estimation technique.
- Further research comparing these methodologies is warranted to fully understand their capabilities in monocular 3D motion sensing.
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