SurgLAT: Surgical Latent Attention Tracking for Depth-Aware Robotic Laparoscope Control
Rulin Zhou, Qiujie Song, Yujie Ma, An Wang, Wanhao Liu, Guoheng Ma, Yidu Wang, Guankun Wang, Xingrong Diao, Jiankun Wang, Chaowei Zhu, Xianming Liu, Hongliang Ren
cs.AI
Submitted: 2026-08-08
Updated: 2026-08-11
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
The gist: Autonomous laparoscopic camera control requires continuous understanding of the surgeon's operative intent in dynamic surgical scenes, where the target operative region is not a stable physical
Terminology
Abstract
Autonomous laparoscopic camera control requires continuous understanding of the surgeon's operative intent in dynamic surgical scenes, where the target operative region is not a stable physical object but a latent and temporally evolving attention state. In this work, we present Surgical Latent Attention Tracking (SurgLAT), a causal online framework for latent surgical attention modeling and autonomous laparoscopic view control. SurgLAT uses a frozen DINOv3 encoder and a state-conditioned spatial token mixer to extract operative evidence under a memory-guided spatial prior, while a selective causal latent memory module jointly models short-term motion continuity and long-horizon surgical intent evolution through dynamic retrieval of current, recent, and historical latent states. The learned latent surgical attention state is decoded into a probabilistic attention heatmap and operative region for downstream endoscope guidance. Beyond perception, we further introduce a robotic deployment framework with explicit laparoscopic Remote Center of Motion (RCM) constrained control based on virtual-axis formulation, together with redundancy-aware null-space initialization for stable and smooth manipulator motion. We validate the full system on real laparoscopic surgical videos and a physical robotic laparoscope platform. Experimental results demonstrate robust online operative-region tracking and stable autonomous endoscopy adjustment under occlusion, rapid motion, and target transitions, highlighting the effectiveness of latent surgical intent modeling for surgical autonomy.
Sources
- SurgAtt-Tracker: Online Surgical Attention Tracking via Temporal Proposal Reranking and Motion-Aware Refinement
- DINOv3
- YOLOv11: An Overview of the Key Architectural Enhancements
- YOLOv12: Attention-Centric Real-Time Object Detectors
- YOLO26: Key Architectural Enhancements and Performance Benchmarking for Real-Time Object Detection
- RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer
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