ABSTRACT For decades, Perspective‐n‐Point (PnP) algorithms have been widely used for camera pose estimation in incremental Structure‐from‐Motion (SfM) systems. The simplest example of PnP problems involves registering a new camera to two already‐registered cameras in the 3D model, which forms a camera triplet and requires at least four 2D‐3D correspondences (i.e., four 3‐view points) to determine the pose of the new camera. However, this requirement is not always satisfied in challenging urban scenarios where only 2‐view points are available due to insufficient overlap between views, leading to incomplete and fractured models. This work revisits the minimal solver that uses as low as six pure 2D correspondences for pose estimation and provides a comprehensive assessment across various scenes. We modify the solver to support both calibrated and uncalibrated cameras and incorporate Bundle Adjustment (BA) for camera triplet refinement. We name the refined solver Triplet‐2‐view‐points (T2vP), as it leverages 2‐view points to estimate the pose of the new camera within the triplet. Extensive experiments on diverse datasets demonstrate substantial improvements in model completeness when T2vP is integrated into existing SfM systems. The results highlight the effectiveness of T2vP in reconstructing challenging urban environments with weak 3‐view overlap.
Huang et al. (2026) studied this question.