References and Further Reading

[COLLAB] Please help curate and prioritize this reference list, and add any additional references.

Foundational Textbooks

  • Hartley, R.I. & Zisserman, A. (2004). Multiple View Geometry in Computer Vision. 2nd edition. Cambridge University Press. — The definitive reference for projective geometry, fundamental/essential matrices, homography, triangulation, and bundle adjustment.

  • Ma, Y., Soatto, S., Kosecka, J., & Sastry, S.S. (2004). An Invitation to 3-D Vision. Springer. — Rigorous treatment of Lie groups and geometric vision with emphasis on SE(3) and optimization on manifolds.

Camera Calibration

  • Zhang, Z. (2000). "A Flexible New Technique for Camera Calibration." IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11), 1330-1334. — Zhang's planar calibration method: intrinsics from homographies via the image of the absolute conic.

  • Brown, D.C. (1966). "Decentering Distortion of Lenses." Photometric Engineering, 32(3), 444-462. — The original Brown-Conrady distortion model with radial and tangential components.

Minimal Solvers

  • Nister, D. (2004). "An Efficient Solution to the Five-Point Relative Pose Problem." IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(6), 756-770. — The 5-point essential matrix solver used in stereo and visual odometry.

  • Kneip, L., Scaramuzza, D., & Siegwart, R. (2011). "A Novel Parametrization of the Perspective-Three-Point Problem for a Direct Computation of Absolute Camera Position and Orientation." IEEE Conference on Computer Vision and Pattern Recognition (CVPR). — The P3P solver used for camera pose estimation.

Hand-Eye Calibration

  • Tsai, R.Y. & Lenz, R.K. (1989). "A New Technique for Fully Autonomous and Efficient 3D Robotics Hand/Eye Calibration." IEEE Transactions on Robotics and Automation, 5(3), 345-358. — The Tsai-Lenz method for solving AX=XB.

Robust Estimation

  • Fischler, M.A. & Bolles, R.C. (1981). "Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography." Communications of the ACM, 24(6), 381-395. — The original RANSAC paper.

  • Hartley, R.I. (1997). "In Defense of the Eight-Point Algorithm." IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(6), 580-593. — Demonstrates that data normalization makes the 8-point algorithm competitive with iterative methods.

Non-Linear Optimization

  • Levenberg, K. (1944). "A Method for the Solution of Certain Non-Linear Problems in Least Squares." Quarterly of Applied Mathematics, 2(2), 164-168.

  • Marquardt, D.W. (1963). "An Algorithm for Least-Squares Estimation of Nonlinear Parameters." Journal of the Society for Industrial and Applied Mathematics, 11(2), 431-441.

  • Triggs, B., McLauchlan, P.F., Hartley, R.I., & Fitzgibbon, A.W. (2000). "Bundle Adjustment — A Modern Synthesis." International Workshop on Vision Algorithms. Springer. — Comprehensive survey of bundle adjustment techniques.

Lie Groups in Vision

  • Sola, J., Deray, J., & Atchuthan, D. (2018). "A Micro Lie Theory for State Estimation in Robotics." arXiv:1812.01537. — Accessible introduction to Lie groups for robotics and vision, covering SO(3), SE(3), and their tangent spaces.