ResearchResearch paperRobotics & Embodied AI · Computer Vision · Multimodal Models1 source · Oct 10, 2026

Multi-modal Representation Fusion for Long-term Microsurgical Anastomosis Trajectory Anticipation

In this work, we propose a cross-modal feature fusion framework for long-term 2D and 3D microsurgical anastomosis trajectory anticipation.

Microsurgical anastomosis is one of the most challenging and sophisticated procedures for surgical robotics, requiring accurate manipulation of surgical instruments within a limited space to achieve precise operations.
Accurate surgical trajectory anticipation is essential for surgical scene understanding, intraoperative planning, robotic manipulation, and reliable behaviour analysis.
Semantic recognition of surgical objects and consecutive motion estimation are incorporated as multi-modal visual cues, providing complementary spatial and temporal guidance for instrument-tissue interaction characterization.
A coarse-to-fine progressive fusion strategy with a long-short-term memory mechanism is introduced to capture temporal dependencies step by step.

[1]Multi-modal Representation Fusion for Long-term Microsurgical Anastomosis Trajectory Anticipation
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 02:39 AM
In this work, we propose a cross-modal feature fusion framework for long-term 2D and 3D microsurgical anastomosis trajectory anticipation.
Microsurgical anastomosis is one of the most challenging and sophisticated procedures for surgical robotics, requiring accurate manipulation of surgical instruments within a limited space to achieve precise operations.

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