Reference

This documentation was generated with the assistance of AI. Please report any inaccuracies.

These reports are generated during the build.

Generated Reports

Report Description

Report	Description
Javadoc	Javadoc API documentation, generated by the `maven-javadoc-plugin`.
Surefire report	Unit test results, generated by `maven-surefire-report-plugin`.
JaCoCo coverage	Code coverage report, generated by the `jacoco-maven-plugin`.
SonarCloud	Project quality dashboard with issue analysis, code quality information, and coverage metrics.
Maven Site	Aggregation of project reports.
GitHub repository	Source code repository for the project.

Javadoc

Javadoc API documentation, generated by the maven-javadoc-plugin.

Surefire report

Unit test results, generated by maven-surefire-report-plugin.

JaCoCo coverage

Code coverage report, generated by the jacoco-maven-plugin.

SonarCloud

Project quality dashboard with issue analysis, code quality information, and coverage metrics.

Maven Site

Aggregation of project reports.

GitHub repository

Source code repository for the project.

External Dashboards

Link	Description
Maven Central	Published release artifacts.
Changelog	Notable changes for each release, in Keep a Changelog format.

Link

Description

Maven Central

Published release artifacts.

Changelog

Notable changes for each release, in Keep a Changelog format.

Bibliography

Bibliographic references discovered in the library’s own javadoc, or supplied as standard external context where the javadoc cites none, while writing the geometric-entity pages (Transformations, Rotations, Points, Lines and Planes, Conics and Quadrics, Triangles and Polygons, Pinhole Camera and Boxes and KD-Trees) and the estimator pages (Estimators, Point, Line and Plane Estimators, Conic and Quadric Estimators, Transformation Estimators and Pinhole Camera Estimators). The three books below (Hartley & Zisserman, the author’s own PhD thesis, and Numerical Recipes) were supplied directly by the repository owner and read chapter-by-chapter to ground the pages above in real equations, proofs and page citations, rather than restating the theory from memory.

Books

Richard Hartley and Andrew Zisserman, Multiple View Geometry in Computer Vision, 2nd edition, Cambridge University Press, 2004. ISBN 9780521540513. Author’s book page.
Not cited directly anywhere in the library’s javadoc; supplied as the primary formal reference for projective geometry, the Euclidean/metric/affine/projective stratification, point-line-plane duality, conics/quadrics and the pinhole camera model. Specific sections used:

Used by Chapter/section/page

Used by	Chapter/section/page
Points, Lines and Planes	§3.2.1-3.2.2 (pp.66-70, point-plane duality , lines self-dual in ); §3.5 Result 3.7 (p.80, fixed under a projective if is an affinity); §3.6 eq. 3.21-3.23 & Result 3.9 (pp.81-82, absolute conic).
Transformations	Table 2.4 (p.44, 2D hierarchy of transformations); Table 3.2 (p.78, 3D hierarchy); §3.5 Result 3.7 + Example 3.8 (p.80, eigenvector analysis of a rotation’s fixed planes); §3.6 Result 3.9 (p.82, fixed iff similarity); §3.7 Result 3.10 (pp.83-84, full matrix proof that is fixed iff similarity).
Conics and Quadrics	Chapter 2, p.31, Result 2.7 (tangent line ) and the derivation; pp.36-37, Results 2.13-2.14 (conic/dual-conic transform rules); p.64, exercise 2.8(viii) ( _\times] as the rank-2 dual conic of a line’s two intersection points with ); chapter 3, §3.2.3-3.2.4 pp.73-74, eq. 3.15-3.17 (quadric duality/transform, second source alongside the PhD thesis); §§3.6-3.7 pp.81-83, Result 3.9 (second source for the absolute conic/dual absolute quadric).
Pinhole Camera	Chapter 6 "Camera Models" §6.1-6.2 (pp.153-165, table 6.1 p.158, Result 6.1 depth pp.162-163, ] decomposition p.164); chapter 8 "More Single View Geometry" §8.3-8.8 (pp.201-225: quadric projection/cone of rays, importance of the camera centre, image of the absolute conic Result 8.17 pp.208-211, vanishing points/lines pp.212-220, affine measurements pp.220-222, calibration from a single view pp.222-225); Appendix 4 §A4.1.1 Algorithm A4.1 (p.579, RQ decomposition by Givens rotations) and §A4.1.2 (p.580, Householder/QR); Appendix 5 §A5.2 (p.590, pseudo-inverse) and Algorithm A5.4 (p.593).
Estimators	Chapter 4 "Estimation - 2D Projective Transformations", §4.7 "Robust estimation" (pp.117-122: Algorithm 4.4 RANSAC p.119, eq. 4.18 sample-count formula p.119, table 4.2 inlier thresholds p.119, Algorithm 4.5 adaptive sample count p.121, eq. 4.19 robust cost function p.120, §4.7.3 Least Median of Squares p.120); Algorithm 4.6 automatic homography estimation via RANSAC (p.123).
Conic and Quadric Estimators	Chapter 2, §2.2 "Five points define a conic" (pp.30-31) — the 5×6 homogeneous linear system and null-space argument for why a conic needs exactly 5 points, matched by `Conic.setParametersFromPoints`.
Transformation Estimators	§4.1 "The Direct Linear Transformation (DLT) algorithm" (pp.88-92: constraint counting showing each point correspondence gives 2 equations, hence 4 points for a 2D homography’s 8 DOF; Algorithm 4.1 basic DLT p.109, Algorithm 4.2 normalized DLT p.109); §4.4 "Transformation invariance and normalization" (pp.104-108, isotropic centroid/average-distance normalization, contrasted with what the library’s own `normalize()` calls actually do); §4.3 and Algorithm 4.3 "The Gold Standard algorithm" (p.114, the linear-then-Levenberg-Marquardt two-stage refinement pattern); Appendix A5.3-A5.4 "Least-squares solution of homogeneous equations" (pp.592-593, the proof that the smallest right singular vector minimizes subject to ).
Pinhole Camera Estimators	Chapter 7 "Computation of the Camera Matrix P" in full: §7.1 "Basic equations" (pp.178-180, eq. 7.1-7.3, the 2×12 camera design-matrix row layout, the "5½ points"/11-equations minimal count, and the line-correspondence extension via back-projection); §7.2 "Geometric error" and Algorithm 7.1 "The Gold Standard algorithm" (pp.180-182, Table 7.1); §7.3 "Restricted camera estimation" (pp.184-186, the assumptions motivating EPnP/UPnP); Appendix A5.1-A5.2 "The pseudo-inverse" and A5.2.1 (pp.589-591, the weighted normal equations behind the Weighted estimators).

Points, Lines and Planes

§3.2.1-3.2.2 (pp.66-70, point-plane duality , lines self-dual in ); §3.5 Result 3.7 (p.80, fixed under a projective if is an affinity); §3.6 eq. 3.21-3.23 & Result 3.9 (pp.81-82, absolute conic).

Transformations

Table 2.4 (p.44, 2D hierarchy of transformations); Table 3.2 (p.78, 3D hierarchy); §3.5 Result 3.7 + Example 3.8 (p.80, eigenvector analysis of a rotation’s fixed planes); §3.6 Result 3.9 (p.82, fixed iff similarity); §3.7 Result 3.10 (pp.83-84, full matrix proof that is fixed iff similarity).

Conics and Quadrics

Chapter 2, p.31, Result 2.7 (tangent line ) and the derivation; pp.36-37, Results 2.13-2.14 (conic/dual-conic transform rules); p.64, exercise 2.8(viii) ( _\times] as the rank-2 dual conic of a line’s two intersection points with ); chapter 3, §3.2.3-3.2.4 pp.73-74, eq. 3.15-3.17 (quadric duality/transform, second source alongside the PhD thesis); §§3.6-3.7 pp.81-83, Result 3.9 (second source for the absolute conic/dual absolute quadric).

Pinhole Camera

Chapter 6 "Camera Models" §6.1-6.2 (pp.153-165, table 6.1 p.158, Result 6.1 depth pp.162-163, ] decomposition p.164); chapter 8 "More Single View Geometry" §8.3-8.8 (pp.201-225: quadric projection/cone of rays, importance of the camera centre, image of the absolute conic Result 8.17 pp.208-211, vanishing points/lines pp.212-220, affine measurements pp.220-222, calibration from a single view pp.222-225); Appendix 4 §A4.1.1 Algorithm A4.1 (p.579, RQ decomposition by Givens rotations) and §A4.1.2 (p.580, Householder/QR); Appendix 5 §A5.2 (p.590, pseudo-inverse) and Algorithm A5.4 (p.593).

Estimators

Chapter 4 "Estimation - 2D Projective Transformations", §4.7 "Robust estimation" (pp.117-122: Algorithm 4.4 RANSAC p.119, eq. 4.18 sample-count formula p.119, table 4.2 inlier thresholds p.119, Algorithm 4.5 adaptive sample count p.121, eq. 4.19 robust cost function p.120, §4.7.3 Least Median of Squares p.120); Algorithm 4.6 automatic homography estimation via RANSAC (p.123).

Conic and Quadric Estimators

Chapter 2, §2.2 "Five points define a conic" (pp.30-31) — the 5×6 homogeneous linear system and null-space argument for why a conic needs exactly 5 points, matched by Conic.setParametersFromPoints.

Transformation Estimators

§4.1 "The Direct Linear Transformation (DLT) algorithm" (pp.88-92: constraint counting showing each point correspondence gives 2 equations, hence 4 points for a 2D homography’s 8 DOF; Algorithm 4.1 basic DLT p.109, Algorithm 4.2 normalized DLT p.109); §4.4 "Transformation invariance and normalization" (pp.104-108, isotropic centroid/average-distance normalization, contrasted with what the library’s own normalize() calls actually do); §4.3 and Algorithm 4.3 "The Gold Standard algorithm" (p.114, the linear-then-Levenberg-Marquardt two-stage refinement pattern); Appendix A5.3-A5.4 "Least-squares solution of homogeneous equations" (pp.592-593, the proof that the smallest right singular vector minimizes subject to ).

Pinhole Camera Estimators

Chapter 7 "Computation of the Camera Matrix P" in full: §7.1 "Basic equations" (pp.178-180, eq. 7.1-7.3, the 2×12 camera design-matrix row layout, the "5½ points"/11-equations minimal count, and the line-correspondence extension via back-projection); §7.2 "Geometric error" and Algorithm 7.1 "The Gold Standard algorithm" (pp.180-182, Table 7.1); §7.3 "Restricted camera estimation" (pp.184-186, the assumptions motivating EPnP/UPnP); Appendix A5.1-A5.2 "The pseudo-inverse" and A5.2.1 (pp.589-591, the weighted normal equations behind the Weighted estimators).

Alberto Irurueta, Fixed Scene 3D Reconstruction for Mobile Applications (PhD thesis / internal research report; the running header printed on every page instead reads "3D Face and Object Reconstruction for Mobile Applications" — same document, apparently a leftover draft header), Visual Engineering. Unpublished; no ISBN/DOI. Authored by, and reproduced here with the permission of, the repository owner — no copyright restrictions apply. Download the original PDF (99 MB).
Conic.java:478 carries an internal TODO citing "page 9 PHD report.pdf": //TODO: intersection of Line2D with Conic results in two points (page 9 PHD report.pdf). Printed page 9 (PDF page 21) is §1.2.6.1 "Line-conic intersections", Proof 1.2 (eq. 1.34-1.44): it proves that a line meeting a conic at a fixed point yields a second point solving . The TODO cites the theoretical basis for a feature that is still genuinely unimplemented, not existing code. Supplied directly by the repository owner as the primary source for chapter 1 ("Projective Geometry") and chapter 2 ("Camera models"). Specific sections used:

Used by Chapter/section/page (printed / PDF, offset +12)

Used by	Chapter/section/page (printed / PDF, offset +12)
Points, Lines and Planes	§1.2.1-1.2.2 (p.2/14, homogeneous↔inhomogeneous conversion, points at infinity when ); §1.2.3 eq. 1.5-1.9 (p.3/15-16, point-line duality , join/meet as cross products, skew-symmetric matrix _\times]); §1.2.4 eq. 1.10-1.23 + Proof 1.1 (pp.4-6/16-18, signed point-to-line distance); §1.3.3 eq. 1.68-1.73 (pp.13-14/25-26, plane via 4×4 determinant/Laplace expansion); §1.3.4 eq. 1.74 (p.14/26, point-plane duality ); §1.4.1.1 eq. 1.118 + fig. 1.9 (pp.22-23/34-35, canonical via 3 vanishing points); §1.4.1.2 + fig. 1.10 (p.23/35, line at infinity ); §1.4.2 eq. 1.119-1.127 (pp.24-25/36-37, proof that affine transforms fix ).
Transformations	§1.4.1.1 (p.23, canonical ); §1.4.2 (p.24, affine invariance proof + upgrade matrix ); §1.4.3.2-1.4.3.3 (pp.29-34, eq. 1.143-1.169, orthogonality-from-first-principles giving , ); §1.4.3.4 "Proof 1.6" (pp.35-36, eq. 1.170-1.178, similarities are exactly the transforms fixing ); §1.4.3.5 (pp.37-40, eq. 1.179-1.195, upgrade matrices , , ); §1.4.5 table 1.4 (pp.41-42, DOF/matrix/invariants summary); §1.3.7 (p.20, eq. 1.109-1.116, general point/plane/quadric/dual-quadric transform rules).
Conics and Quadrics	§1.2.5 "Conics" eq. 1.24-1.27 + table 1.1 (pp.6-8/18-20); §1.2.6.1 "Line-conic intersections" Proof 1.2, eq. 1.34-1.44 (pp.8-9/20-21) — resolves the `Conic.java:478` TODO above; §1.2.6.2 "Line-conic tangency" eq. 1.46-1.51 (pp.10-11/22-23, tangent line ); §1.2.6.3 "Relation between conic and dual conic" eq. 1.52-1.54 (p.11/23, ); §1.2.7 "Transforming conics and dual conics" eq. 1.55-1.63 + Proof 1.3 (pp.11-12/23-24); §1.3.5 "Quadrics" eq. 1.75-1.91 + tables 1.2-1.3 (pp.15-16/27-28); §1.3.6.1-1.3.7 eq. 1.92-1.116 (pp.17-21/29-33, plane-quadric intersection/tangency, , quadric transform rule); §1.4.3.1-1.4.3.3 eq. 1.137-1.169 (pp.28-34/40-46, canonical absolute-conic/dual-quadric factories).
Pinhole Camera	Chapter 2 "Camera models" in full, pp.43-89/56-100: §2.1-2.2 canonical camera (pp.44-50); §2.2.2-2.2.3 intrinsics/CCD pixel conversion (pp.47-52); §2.2.4 radial distortion (pp.52-54); §2.3.1.1-1.3 the three camera-center methods — SVD, cofactors/determinants, closed-form (pp.55-58); §2.3.2-2.3.5 vanishing points, axis/principal planes, principal point/axis, camera sign (pp.59-63); §2.3.6 RQ-style decomposition into and (p.64); §2.4 depth and cheirality (pp.64-68); §2.5 DLT camera estimation (pp.68-74); §2.6 point triangulation (pp.77-81); §2.7 back-projection of points and lines (pp.82-83); §2.8 "Camera matrices, the geometry strata and their relation to conics and quadrics" (pp.83-85); §2.9 camera matrices and image homographies (pp.86-88).
Estimators	§2.5.2 "Robust camera estimation using RANSAC" (printed pp.75-77) — an independently-derived RANSAC sample-count formula (its own "Algorithm 2.1", eq. 2.125-2.132) matching Hartley & Zisserman’s eq. 4.18, worked out by the same author who wrote this library.
Pinhole Camera Estimators	§2.5.1 "The DLT algorithm for camera estimation" (pp.68-71) and §2.5.1.1 "LMSE solution" (pp.71-72) — the exact DLT-for-cameras construction `DLTPointCorrespondencePinholeCameraEstimator` implements; §2.5.2 (pp.75-77) reused for the RANSAC derivation above.

Points, Lines and Planes

§1.2.1-1.2.2 (p.2/14, homogeneous↔inhomogeneous conversion, points at infinity when ); §1.2.3 eq. 1.5-1.9 (p.3/15-16, point-line duality , join/meet as cross products, skew-symmetric matrix _\times]); §1.2.4 eq. 1.10-1.23 + Proof 1.1 (pp.4-6/16-18, signed point-to-line distance); §1.3.3 eq. 1.68-1.73 (pp.13-14/25-26, plane via 4×4 determinant/Laplace expansion); §1.3.4 eq. 1.74 (p.14/26, point-plane duality ); §1.4.1.1 eq. 1.118 + fig. 1.9 (pp.22-23/34-35, canonical via 3 vanishing points); §1.4.1.2 + fig. 1.10 (p.23/35, line at infinity ); §1.4.2 eq. 1.119-1.127 (pp.24-25/36-37, proof that affine transforms fix ).

Transformations

§1.4.1.1 (p.23, canonical ); §1.4.2 (p.24, affine invariance proof + upgrade matrix ); §1.4.3.2-1.4.3.3 (pp.29-34, eq. 1.143-1.169, orthogonality-from-first-principles giving , ); §1.4.3.4 "Proof 1.6" (pp.35-36, eq. 1.170-1.178, similarities are exactly the transforms fixing ); §1.4.3.5 (pp.37-40, eq. 1.179-1.195, upgrade matrices , , ); §1.4.5 table 1.4 (pp.41-42, DOF/matrix/invariants summary); §1.3.7 (p.20, eq. 1.109-1.116, general point/plane/quadric/dual-quadric transform rules).

Conics and Quadrics

§1.2.5 "Conics" eq. 1.24-1.27 + table 1.1 (pp.6-8/18-20); §1.2.6.1 "Line-conic intersections" Proof 1.2, eq. 1.34-1.44 (pp.8-9/20-21) — resolves the Conic.java:478 TODO above; §1.2.6.2 "Line-conic tangency" eq. 1.46-1.51 (pp.10-11/22-23, tangent line ); §1.2.6.3 "Relation between conic and dual conic" eq. 1.52-1.54 (p.11/23, ); §1.2.7 "Transforming conics and dual conics" eq. 1.55-1.63 + Proof 1.3 (pp.11-12/23-24); §1.3.5 "Quadrics" eq. 1.75-1.91 + tables 1.2-1.3 (pp.15-16/27-28); §1.3.6.1-1.3.7 eq. 1.92-1.116 (pp.17-21/29-33, plane-quadric intersection/tangency, , quadric transform rule); §1.4.3.1-1.4.3.3 eq. 1.137-1.169 (pp.28-34/40-46, canonical absolute-conic/dual-quadric factories).

Pinhole Camera

Chapter 2 "Camera models" in full, pp.43-89/56-100: §2.1-2.2 canonical camera (pp.44-50); §2.2.2-2.2.3 intrinsics/CCD pixel conversion (pp.47-52); §2.2.4 radial distortion (pp.52-54); §2.3.1.1-1.3 the three camera-center methods — SVD, cofactors/determinants, closed-form (pp.55-58); §2.3.2-2.3.5 vanishing points, axis/principal planes, principal point/axis, camera sign (pp.59-63); §2.3.6 RQ-style decomposition into and (p.64); §2.4 depth and cheirality (pp.64-68); §2.5 DLT camera estimation (pp.68-74); §2.6 point triangulation (pp.77-81); §2.7 back-projection of points and lines (pp.82-83); §2.8 "Camera matrices, the geometry strata and their relation to conics and quadrics" (pp.83-85); §2.9 camera matrices and image homographies (pp.86-88).

Estimators

§2.5.2 "Robust camera estimation using RANSAC" (printed pp.75-77) — an independently-derived RANSAC sample-count formula (its own "Algorithm 2.1", eq. 2.125-2.132) matching Hartley & Zisserman’s eq. 4.18, worked out by the same author who wrote this library.

Pinhole Camera Estimators

§2.5.1 "The DLT algorithm for camera estimation" (pp.68-71) and §2.5.1.1 "LMSE solution" (pp.71-72) — the exact DLT-for-cameras construction DLTPointCorrespondencePinholeCameraEstimator implements; §2.5.2 (pp.75-77) reused for the RANSAC derivation above.

William H. Press, Saul A. Teukolsky, William T. Vetterling and Brian P. Flannery, Numerical Recipes: The Art of Scientific Computing, 3rd edition, Cambridge University Press, 2007. numerical.recipes.
Not cited in the source. Used for two distinct purposes:

Used by Chapter/section/page

Used by	Chapter/section/page
Boxes and KD-Trees	Chapter 21 "Computational Geometry", §21.2 "KD Trees and Nearest-Neighbor Finding" (printed p.1101, PDF p.1125). KDTree’s internal structure and naming (`mom/`dau1`/`dau2`, `selecti`, `siftDown`, `BIG`) closely mirrors this section’s classic k-d tree implementation — an inference from the code’s structure, not a citation present in the source.
Pinhole Camera	§2.6 "Singular Value Decomposition" (printed pp.65-67, PDF p.89) — backs Camera.backProject(Point2D, Point3D)’s Moore-Penrose pseudo-inverse and `PinholeCamera.computeCameraCenterSVD(); §2.10 "QR Decomposition" (printed pp.102-106, PDF p.126) — the general algorithm family that Hartley & Zisserman’s Appendix 4 RQ decomposition (used by `RQDecomposer`) specializes.
Conic and Quadric Estimators, Transformation Estimators, Pinhole Camera Estimators	§2.6 "Singular Value Decomposition" (printed pp.65-67, PDF p.89) — reused citation for the same SVD null-space technique behind `Conic`/`Quadric’s minimal-sample solve, the affine/projective transformation DLT-style solve, and the camera DLT solve, respectively.

Boxes and KD-Trees

Chapter 21 "Computational Geometry", §21.2 "KD Trees and Nearest-Neighbor Finding" (printed p.1101, PDF p.1125). KDTree’s internal structure and naming (`mom/dau1/dau2, selecti, siftDown, BIG) closely mirrors this section’s classic k-d tree implementation — an inference from the code’s structure, not a citation present in the source.

Pinhole Camera

§2.6 "Singular Value Decomposition" (printed pp.65-67, PDF p.89) — backs Camera.backProject(Point2D, Point3D)’s Moore-Penrose pseudo-inverse and `PinholeCamera.computeCameraCenterSVD(); §2.10 "QR Decomposition" (printed pp.102-106, PDF p.126) — the general algorithm family that Hartley & Zisserman’s Appendix 4 RQ decomposition (used by RQDecomposer) specializes.

Conic and Quadric Estimators, Transformation Estimators, Pinhole Camera Estimators

§2.6 "Singular Value Decomposition" (printed pp.65-67, PDF p.89) — reused citation for the same SVD null-space technique behind Conic/`Quadric’s minimal-sample solve, the affine/projective transformation DLT-style solve, and the camera DLT solve, respectively.

Joseph O’Rourke, Computational Geometry in C, 2nd edition, Cambridge University Press, 1998 (chapter 1 covers polygon triangulation, including ear clipping).
Author’s book page.
Not cited in the source; supplied as standard context for the ear-clipping ("Van Gogh") triangulation algorithm. Used by: Triangles and Polygons.

Papers

James Diebel, Representing Attitude: Euler Angles, Unit Quaternions, and Rotation Vectors, Stanford University, 2006. Cited directly in MatrixRotation3D javadoc (mirrored there at staff.city.ac.uk/~sbbh653/publications/euler.pdf); a current copy is available at astro.rug.nl. Used by: Rotations.

Joan Solà, Quaternion kinematics for the error-state Kalman filter, arXiv:1711.02508, 2017. arxiv.org/abs/1711.02508. The formal, citable write-up of the MATLAB slamtb functions referenced throughout the source (see [bib-sola-slamtb]). Used by: Rotations.

S. W. Shepperd, "Quaternion from Rotation Matrix", Journal of Guidance and Control, Vol. 1, No. 3, 1978, pp. 223-224.
AIAA ARC (DOI).
Implemented by Quaternion.matrixRotationToQuaternion (branch selection by matrix trace). Used by: Rotations.

Wolfgang Kabsch, "A solution for the best rotation to relate two sets of vectors", Acta Crystallographica, Section A, 32(5), 1976, pp. 922-923.
The optimal-rotation algorithm cited directly in `EuclideanTransformation2D/3DEstimator’s javadoc (via Wikipedia’s Kabsch algorithm article) and implemented by those two classes. Used by: Transformation Estimators.

Adrian Penate-Sánchez, Francesc Moreno-Noguer and Juan Andrade-Cetto, "Exhaustive Linearization for Robust Camera Pose and Focal Length Estimation", PDF (iri.upc.edu).
The UPnP (Uncalibrated Perspective-n-Point) paper, cited directly in UPnPPointCorrespondencePinholeCameraEstimator javadoc together with the PDF link above. Used by: Pinhole Camera Estimators.

Vincent Lepetit, Francesc Moreno-Noguer and Pascal Fua, "EPnP: An Accurate O(n) Solution to the PnP Problem", International Journal of Computer Vision, 81(2), 2009, pp. 155-166. PDF (tugraz.at). Cited directly in EPnPPointCorrespondencePinholeCameraEstimator javadoc (the estimators package). Used by: Pinhole Camera, Pinhole Camera Estimators.

Martin A. Fischler and Robert C. Bolles, "Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography", Communications of the ACM, 24(6), 1981, pp. 381-395.
ACM Digital Library (DOI).
The original RANSAC paper. Not cited directly in the library’s javadoc, but every RANSACXxxRobustEstimator class across all twelve estimator hierarchies in com.irurueta.geometry.estimators (and the generic com.irurueta.numerical.robust.RANSACRobustEstimator<T> they delegate to) implements this algorithm. Also cited by name inside Hartley & Zisserman chapter 4 (Algorithm 4.4, p.119), which is the more detailed source actually used for the equations on Estimators. Used by: Estimators.

Philip H. S. Torr and Andrew Zisserman, "MLESAC: A New Robust Estimator with Application to Estimating Image Geometry", Computer Vision and Image Understanding, 78(1), 2000, pp. 138-156.
PDF (robots.ox.ac.uk).
Formalizes the MSAC (M-estimator SAmple Consensus) robust cost function as an intermediate step towards MLESAC. Not cited directly in the library’s javadoc; supplied as the source for the name "MSAC" behind every MSACXxxRobustEstimator class, whose scoring rule matches the un-named robust cost function already given in Hartley & Zisserman eq. 4.19 (p.120). Used by: Estimators.

Ondřej Chum and Jiří Matas, "Matching with PROSAC — Progressive Sample Consensus", Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2005, pp. 220-226.
PDF (cmp.felk.cvut.cz).
The PROSAC paper. Not cited directly in the library’s javadoc; postdates Hartley & Zisserman's 2nd edition (2004) and is not covered by any of the three source PDFs used for this documentation pass — supplied as external context for every PROSACXxxRobustEstimator class’s quality-score-ordered sampling strategy. No equally-named published reference was found for this library’s PROMedS variant; it is documented as the library’s own LMedS analogue of the same ordering idea. Used by: Estimators.

Web resources and code

Martin Baker, axis-angle rotation reference code, euclideanspace.com. Cited directly in AxisRotation3D javadoc as the basis for its implementation. Used by: Rotations.

Joan Solà, SLAM toolbox MATLAB functions (au2q.m, qProd.m, e2R.m, q2au.m, R2q.m, w2omega.m, and others), github.com/joansola/slamtb. Cited dozens of times across Quaternion.java, MatrixRotation3D.java and RotationUtils.java. See also [bib-sola-arxiv] for the formal paper version of the same material. Used by: Rotations.

Wikipedia, "Ellipse", en.wikipedia.org/wiki/Ellipse. Cited directly in Ellipse.java javadoc as the source of its formulas. Used by: Conics and Quadrics.

Knud Thomsen’s ellipsoid surface-area approximation (exponent p ≈ 1.6075, first proposed 2004), used by Ellipsoid.getSurface(). Not cited in the source; see e.g. John D. Cook’s write-up for a derivation and accuracy discussion (error margin below ~1.18%). Used by: Conics and Quadrics.