Luca Carlini
Papers
1
Total Citations
4
H-Index
1
About
Luca Carlini is a leading researcher at the intersection of computer vision and robotic-assisted surgery (RAS). His work focuses on enabling precise 3D perception in minimally invasive procedures, particularly through depth estimation—a critical capability for surgical navigation and autonomous robotics. Carlini’s most cited paper, “DARES: Depth Anything in Robotic Endoscopic Surgery with Self-supervised Vector-LoRA of the Foundation Model” (2025, 4 citations), introduces a novel approach that adapts large-scale foundation models like Depth Anything Models (DAM) to the surgical domain. Rather than relying on full fine-tuning—which often fails with limited medical data—Carlini’s method uses self-supervised vector-based Low-Rank Adaptation (Vector-LoRA) to preserve the model’s general knowledge while efficiently learning surgical-specific depth cues. This breakthrough addresses a key challenge in RAS: achieving robust, real-time depth estimation from endoscopic video without extensive labeled datasets. With a growing citation impact, Carlini’s work is shaping the next generation of intelligent surgical tools, bridging the gap between general-purpose AI and specialized clinical applications. His contributions are paving the way for safer, more autonomous robotic surgery.
Research Focus
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Top Papers
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