Equivalent Constraints for Two-View Geometry: Pose Solution/Pure Rotation Identification and 3D Reconstruction
Qi Cai, Yuanxin Wu, Lilian Zhang, Peike Zhang

TL;DR
This paper introduces new geometric constraints for two-view pose estimation and 3D reconstruction that simplify the process, eliminate the need for 3D reconstruction to identify the correct pose, and provide robust pure rotation detection.
Contribution
It proposes equivalent pose-only constraints that allow direct pose solution and 3D reconstruction without traditional SVD-based methods, and offers a robust criterion for pure rotation identification.
Findings
Explicitly derived complete pose solutions of the essential matrix.
Validated robustness of the new constraints through experiments.
Achieved analytical 3D reconstruction from identified pose.
Abstract
Two-view relative pose estimation and structure reconstruction is a classical problem in computer vision. The typical methods usually employ the singular value decomposition of the essential matrix to get multiple solutions of the relative pose, from which the right solution is picked out by reconstructing the three-dimension (3D) feature points and imposing the constraint of positive depth. This paper revisits the two-view geometry problem and discovers that the two-view imaging geometry is equivalently governed by a Pair of new Pose-Only (PPO) constraints: the same-side constraint and the intersection constraint. From the perspective of solving equation, the complete pose solutions of the essential matrix are explicitly derived and we rigorously prove that the orientation part of the pose can still be recovered in the case of pure rotation. The PPO constraints are simplified and…
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Taxonomy
MethodsEntropy Regularization · Proximal Policy Optimization
