Absolute and Relative Pose Estimation in Refractive Multi View
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Absolute and Relative Pose Estimation in Refractive Multi View. / Hu, Xiao; Lauze, Francois; Pedersen, Kim Steenstrup; Melou, Jean.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). IEEE, 2021. p. 2569-2578.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Absolute and Relative Pose Estimation in Refractive Multi View
AU - Hu, Xiao
AU - Lauze, Francois
AU - Pedersen, Kim Steenstrup
AU - Melou, Jean
PY - 2021
Y1 - 2021
N2 - This paper investigates absolute and relative pose estimation under refraction, which are essential problems for refractive structure from motion. We first present an absolute pose estimation algorithm by leveraging an efficient iterative refinement. Then, we derive a novel refractive epipolar constraint for relative pose estimation. The epipolar constraint is established based on the virtual camera transformation, making it in a succinct form and can be efficiently optimized. Evaluations of the proposed algorithms on synthetic data show superior accuracy and computational efficiency to state-of-the-art methods. For further validation, we demonstrate the performance on real data and show the application in 3D reconstruction of objects under refraction.
AB - This paper investigates absolute and relative pose estimation under refraction, which are essential problems for refractive structure from motion. We first present an absolute pose estimation algorithm by leveraging an efficient iterative refinement. Then, we derive a novel refractive epipolar constraint for relative pose estimation. The epipolar constraint is established based on the virtual camera transformation, making it in a succinct form and can be efficiently optimized. Evaluations of the proposed algorithms on synthetic data show superior accuracy and computational efficiency to state-of-the-art methods. For further validation, we demonstrate the performance on real data and show the application in 3D reconstruction of objects under refraction.
U2 - 10.1109/ICCVW54120.2021.00290
DO - 10.1109/ICCVW54120.2021.00290
M3 - Article in proceedings
SN - 978-1-6654-0191-3
SP - 2569
EP - 2578
BT - Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
PB - IEEE
T2 - 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)
Y2 - 11 October 2021 through 17 October 2021
ER -
ID: 287177915