pose_estimation op• Data kinds: points × keypoints → pose
• Call: import fullseye as fs; fs.ledger.dlt_pose(points_3d, points_2d, K) (to call the implementation directly, import pnp3d; pnp3d.dlt_pose(points_3d, points_2d, K); from the registry, ops3d.get("dlt_pose"))
Recover the camera pose from 3-D–2-D correspondences by DLT (K known). → (R (3,3), t (3,)). At least six points are needed.
The (R, t) part of pnp_pose (normalised DLT + planar PnP branch + LM refinement). If you also need the reprojection RMS, use pnp_pose. Coplanar input (checkerboards etc.) is routed to planar PnP automatically (the old implementation rejected it fail-closed, but it can be solved correctly, so it is solved).
Procedure (calls the module function pnp3d.pnp_pose with refine=True):
• Pixels are converted to normalised image coordinates with K⁻¹, 3D points are Hartley-normalised (centroid at the origin, mean distance √3), and the DLT (SVD with 12 unknowns) is solved.
• If the coplanarity (square root of the smallest/largest eigenvalue ratio of the covariance) is below 0.05, planar PnP (homography decomposition) is also added as a candidate; if strictly planar (3rd extent is 0), the DLT is skipped.
• Each candidate is refined by Levenberg-Marquardt on the reprojection error (at most 30 iterations), and the candidate with the largest fraction of points in front (depth > 0), and on a tie the smallest reprojection RMS, is taken.
Returns: R (3,3) rotation (det=+1), t (3,) (same unit as world coordinates). Convention Xc = R X + t, x ≅ K Xc.
Raises ValueError: points_2d is not (N,2) (passing an image is rejected by name) / points_3d is not (N,3) / non-finite / fewer than 6 points / point counts mismatch / all points on one line or at one point.
Caution: defenceless against outliers (uses all points equally). If there are false correspondences, use pnp_ransac. Deterministic (no randomness). Evaluate with reprojection_error; compare with GT using pose_error.
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
• pnp_pose_outliers — py -3.11 examples_3d/pnp_pose_outliers.py
• pose_estimation — py -3.11 examples_3d/pose_estimation.py
pose as input)fuse_to_voxel · pose_error · bundle_adjust · mean_reprojection_error · optimize_pose_graph · relative_pose · mean_edge_error · rotation_translation_error
pose_estimation)pnp_ransac · reprojection_error
*Provenance: pnp3d.py — 3D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.