Monov

Monov is a fictional open-standard framework for monocular computer vision designed to simplify integration of single-camera perception in robotics and augmented reality. It provides a lightweight data model and a plugin-based architecture that supports camera calibration, feature extraction, motion estimation, and monocular depth inference. The framework emphasizes interoperability across hardware and software by offering a consistent API and reference implementations in C++ and Python, along with bindings to popular machine learning libraries.

Origin and naming: The name Monov is derived from "mono" and "vision" and is attributed to a collaboration among university labs and open-source contributors in the late 2010s. While not a formal standards body, Monov served as a focal point for discussion about unifying monocular perception interfaces.

Usage and impact: Monov has been used in academic demonstrations and hobbyist robotics platforms to prototype autonomous navigation stacks and AR experiences that rely on single-camera input. It also provides a benchmarking suite to compare monocular depth estimation methods, feature tracking, and real-time performance across devices.

Limitations: Depth estimation from a single camera is ill-posed and sensitive to lighting, texture, and motion. Proponents of Monov emphasize the need for calibration accuracy and sometimes fusion with inertial or stereo data to improve reliability.

See also: monocular vision, depth estimation, SLAM, computer vision software.