  Surgical scene perception via videos is critical for advancing robotic
surgery, telesurgery, and AI-assisted surgery, particularly in ophthalmology.
However, the scarcity of diverse and richly annotated video datasets has
hindered the development of intelligent systems for surgical workflow analysis.
Existing datasets face challenges such as small scale, lack of diversity in
surgery and phase categories, and absence of time-localized annotations. These
limitations impede action understanding and model generalization validation in
complex and diverse real-world surgical scenarios. To address this gap, we
introduce OphNet, a large-scale, expert-annotated video benchmark for
ophthalmic surgical workflow understanding. OphNet features: 1) A diverse
collection of 2,278 surgical videos spanning 66 types of cataract, glaucoma,
and corneal surgeries, with detailed annotations for 102 unique surgical phases
and 150 fine-grained operations. 2) Sequential and hierarchical annotations for
each surgery, phase, and operation, enabling comprehensive understanding and
improved interpretability. 3) Time-localized annotations, facilitating temporal
localization and prediction tasks within surgical workflows. With approximately
285 hours of surgical videos, OphNet is about 20 times larger than the largest
existing surgical workflow analysis benchmark. Code and dataset are available
at: https://minghu0830.github.io/OphNet-benchmark/.
