Global Optimization

The core camera solver assumes known camera intrinsics. When intrinsic parameters such as focal length are unknown or only approximate, the Global Optimization step wraps the core solver to search for the values that produce the best reconstruction.

Overview

Global optimization treats the core camera solver as a black box. It repeatedly runs the full solve pipeline with different candidate parameter values, searching for the parameters that minimize overall reprojection error across the entire sequence.

This is computationally expensive – each evaluation requires a complete camera solve – but it allows the solver to recover parameters that cannot be estimated within the incremental SfM pipeline itself.

Search Strategies

Two search strategies are available:

Differential Evolution (DE) – A population-based evolutionary optimization algorithm. A population of candidate parameter sets evolves over multiple generations, with mutation and crossover operators exploring the search space. Supports coarse-then-refined passes: a broad initial search narrows the parameter range, followed by a finer search within the narrowed bounds. This is generally the preferred strategy for efficiency.

Uniform Grid Search (UGS) – Exhaustive evaluation at regular intervals across the parameter range. Simpler and easier to reason about, but slower than Differential Evolution for high-dimensional or wide-range searches.

Parameters

Currently, the global optimization step supports searching for focal length (applied uniformly across the sequence).

Further detail on configuration, parameter ranges, and performance characteristics will be added in future.

See Also

• Camera Solver Overview – The core camera solver pipeline that is evaluated at each optimization step.