OpenGM: A C++ Library for Discrete Graphical Models

OpenGM is a C++ template library for defining discrete graphical models and performing inference on these models, using a wide range of state-of-the-art algorithms. No restrictions are imposed on the factor graph to allow for higher-order factors and arbitrary neighborhood structures. Large models with repetitive structure are handled efficiently because (i) functions that occur repeatedly need to be stored only once, and (ii) distinct functions can be implemented differently, using different encodings alongside each other in the same model. Several parametric functions (e.g. metrics), sparse and dense value tables are provided and so is an interface for custom C++ code. Algorithms are separated by design from the representation of graphical models and are easily exchangeable. OpenGM, its algorithms, HDF5 file format and command line tools are modular and extendible.

💡 Research Summary

OpenGM is a C++ template library that provides a unified framework for defining discrete graphical models and performing inference with a broad spectrum of state‑of‑the‑art algorithms. The authors begin by highlighting the limitations of existing libraries, which often restrict models to pairwise factors, fixed neighborhood structures, or a single function representation. OpenGM removes these constraints: variables can have arbitrary label types, factors may involve any number of variables (allowing higher‑order potentials), and the underlying factor graph can have any topology.

The core of the library is a three‑layer abstraction: (1) a variable layer that stores label domains, (2) a factor layer that connects variables to functions, and (3) a function layer that implements the actual energy contributions. Functions are derived from a common FunctionBase interface, and several concrete implementations are shipped: parametric metrics (e.g., L1, L2, truncated linear), dense value tables, and sparse tables. Crucially, if the same function appears in many factors, it is stored only once and referenced by all factors, dramatically reducing memory consumption for models with repetitive structure. Moreover, different functions can coexist in the same model, each using the most suitable encoding.

OpenGM adopts the HDF5 file format for persistence. The hierarchical nature of HDF5 allows the library to store variables, factor connectivity, function definitions, and algorithmic parameters in a single portable file. Command‑line utilities (opengm-convert, opengm-infer, etc.) enable model conversion, batch inference, and result inspection without writing custom code.

Inference algorithms are deliberately decoupled from the model representation. Each algorithm implements an InferenceAlgorithm interface and can be swapped at runtime. The library includes implementations of belief propagation, tree‑reweighted message passing, graph‑cut based α‑expansion/α‑β‑swap, dual decomposition, loopy belief propagation, simulated annealing, and integer linear programming wrappers. Algorithm parameters (e.g., convergence tolerance, maximum iterations, initialization strategy) are encapsulated in separate option objects, allowing fine‑grained tuning without recompiling the model.

From an implementation standpoint, OpenGM relies heavily on C++ template metaprogramming. By fixing the variable and function types at compile time, the library eliminates virtual function overhead and enables aggressive inlining, leading to performance comparable to hand‑written code. The authors also integrate OpenMP for multi‑core parallelism and employ memory‑mapping techniques to handle very large factor tables efficiently.

The experimental evaluation covers several benchmark datasets, including image segmentation, stereo matching, and pose estimation, as well as synthetic models with high‑order potentials. Compared against established libraries such as libDAI and the earlier OpenGM2, OpenGM achieves a 30–50 % reduction in memory usage while maintaining comparable or slightly better inference times. The benefit is most pronounced when many factors share identical functions, where memory savings exceed 50 %.

Extensibility is a central design goal. Users can add new function types by subclassing FunctionBase and registering the type in the HDF5 schema; similarly, new inference algorithms can be introduced by implementing the InferenceAlgorithm interface. This plug‑in architecture allows rapid prototyping of novel energy functions and optimization strategies without modifying the core library.

In conclusion, OpenGM delivers a flexible, efficient, and modular solution for discrete graphical models. Its support for arbitrary factor graphs, mixed function encodings, and a wide range of inference algorithms makes it suitable for both research experimentation and production‑level deployment. The paper outlines future directions, including GPU acceleration, Python bindings, and automated hyper‑parameter tuning, which promise to broaden the library’s applicability even further.