TY - GEN
T1 - General Planar Motion from a Pair of 3D Correspondences
AU - Dibene, Juan Carlos
AU - Min, Zhixiang
AU - Dunn, Enrique
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - We present a novel 2-point method for estimating the relative pose of a camera undergoing planar motion from 3D data (e.g. from a calibrated stereo setup or an RGBD sensor). Unlike prior art, our formulation does not assume knowledge of the plane of motion, (e.g. parallelism between the optical axis and motion plane) to resolve the under-constrained nature of SE(3) motion estimation in this context. Instead, we enforce geometric constraints identifying, in closed-form, a unique planar motion solution from an orbital set of geometrically consistent SE(3) motion estimates. We explore the set of special and degenerate geometric cases arising from our formulation. Experiments on synthetic data characterize the sensitivity of our estimation framework to measurement noise and different types of observed motion. We integrate our solver within a RANSAC framework and demonstrate robust operation on standard benchmark sequences of real-world imagery. Code is available at: https://github.com/jdibenes/gpm.
AB - We present a novel 2-point method for estimating the relative pose of a camera undergoing planar motion from 3D data (e.g. from a calibrated stereo setup or an RGBD sensor). Unlike prior art, our formulation does not assume knowledge of the plane of motion, (e.g. parallelism between the optical axis and motion plane) to resolve the under-constrained nature of SE(3) motion estimation in this context. Instead, we enforce geometric constraints identifying, in closed-form, a unique planar motion solution from an orbital set of geometrically consistent SE(3) motion estimates. We explore the set of special and degenerate geometric cases arising from our formulation. Experiments on synthetic data characterize the sensitivity of our estimation framework to measurement noise and different types of observed motion. We integrate our solver within a RANSAC framework and demonstrate robust operation on standard benchmark sequences of real-world imagery. Code is available at: https://github.com/jdibenes/gpm.
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U2 - 10.1109/ICCV51070.2023.00740
DO - 10.1109/ICCV51070.2023.00740
M3 - Conference contribution
AN - SCOPUS:85185871491
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 8026
EP - 8036
BT - Proceedings - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
T2 - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
Y2 - 2 October 2023 through 6 October 2023
ER -