Aug 8, 2026 · cs.AIJ/K move · Enter open · S save
Rulin Zhou, Qiujie Song, Yujie Ma, An Wang+9
Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong SAR 999077, China · Shenzhen Loop Area Institute, Shen Zhen 518055, China · College of Mechatronics and Engineering, Shenzhen University, Shenzhen 518060, China · Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen 518055, China · Shenzhen People’s Hospital, Shenzhen 518020, China
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.