General Self-Calibration with Varying Intrinsics

We address multiview self-calibration from image correspon-dences when camera intrinsic parameters vary across views. While Kruppaequations constrain an implicit matrix encoding of the camera intrinsics(called the dual image of the absolute conic; DIAC), practical calibrationpriors are expressed directly in intrinsic-parameter space. We formulatesuch priors algebraically and map them into DIAC space, yielding explicitconstraints that integrate directly with Kruppa relations. Unlike priorself-calibration methods that focus on specific varying-intrinsic regimes(e.g. shared focal length, fixed aspect, or zero skew), we provide a unifiedalgebraic framework that handles arbitrary intrinsic priors expressed aspolynomial constraints in the space of intrinsics, yielding a more flex-ible formulation of varying-intrinsic self-calibration. Since Kruppa sys-tems are projective and often algebraically dependent, we algorithmicallyconstruct locally independent square subsystems via Jacobian analysis toassess solvability and algebraic complexity under varying intrinsics. Ex-periments demonstrate parity with a few previous classical approachesin shared-focal settings as well as shared-principal-point settings andenable stable estimation for varying-intrinsic configurations previouslyconsidered unsolved, validated on synthetic and real dynamic-intrinsicsequences. Code is available at https://github.com/Rowing0914/Self-Calibration-Varying-Intrinsics.