Institution profile

Intuitive Surgical Inc.

Industry researchnorthamerica · us
Official website
Research library18linked papers
Opportunities0open roles
Selected work

Representative Papers

Intuitive Surgical SurgToolLoc Challenge Results: 2022-2023

May 11, 2023

To address the challenge of real-time, robust surgical instrument localization in minimally invasive robotic-assisted surgery (RAS) video streams, this work introduces SurgToolLoc—the first large-scale, multi-view, multi-scenario benchmark dataset with pixel-level mask annotations. We further propose a novel evaluation protocol emphasizing both cross-center generalizability and real-time inference (≥30 FPS). Methodologically, we integrate instance segmentation and keypoint detection with temporal modeling (ConvLSTM/Transformer), domain adaptation, and weakly supervised learning. Our best-performing model achieves 92.4% mAP@0.5 on the test set while maintaining an inference speed of 36 FPS—substantially outperforming conventional template matching and early CNN-based approaches. The solution has undergone rigorous preclinical validation across multiple surgical scenarios. By providing a reproducible, scalable, end-to-end framework for visual instrument localization in RAS, this work establishes a new standard for benchmarking and advancing vision-based surgical navigation systems.

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Point Tracking in Surgery--The 2025 Surgical Tattoos in Infrared Challenge (STIRC2025)

Jul 14, 2026

This work addresses the longstanding absence of a unified benchmark for keypoint tracking in surgical settings, a critical capability for downstream tasks such as segmentation and 3D reconstruction. To bridge this gap, the authors initiated and organized the STIRC2025 Challenge, establishing the first public evaluation platform dedicated to infrared point tracking in surgical scenarios. Built upon the newly released Surgical Tattoo Infrared (STIR) dataset, the benchmark systematically evaluates algorithmic performance across both in vivo and ex vivo sequences, emphasizing tracking accuracy and inference efficiency. The challenge attracted seven participating teams and provided standardized data, evaluation metrics, and a low-latency testing framework. This effort successfully established a reproducible benchmark, significantly advancing the development of surgical visual perception algorithms.

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Track2Map: Online Deformable SLAM with Motion-Aware Pose Optimization in Robotic Surgery

Jul 09, 2026

This work addresses the limitations of existing Gaussian splatting methods in robot-assisted minimally invasive surgery, which rely on offline processing and accurate prior camera trajectories, rendering them ineffective under missing priors or noisy conditions. The paper proposes an online 3D Gaussian splatting SLAM framework that jointly optimizes camera poses and deformable anatomical structures from monocular or stereo surgical videos, enabling real-time reconstruction without dependable trajectory priors. Key innovations include deformation initialization from dense 2D point trajectories, statistical trajectory analysis to disentangle camera motion from tissue deformation for drift suppression, and a static-camera interval detection mechanism. Evaluated on the StereoMIS dataset, the method outperforms both existing SLAM approaches and non-SLAM methods that depend on trajectory priors in terms of reconstruction fidelity and trajectory accuracy.

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Recent publications

Latest Papers

Point Tracking in Surgery--The 2025 Surgical Tattoos in Infrared Challenge (STIRC2025)

Jul 14, 2026

This work addresses the longstanding absence of a unified benchmark for keypoint tracking in surgical settings, a critical capability for downstream tasks such as segmentation and 3D reconstruction. To bridge this gap, the authors initiated and organized the STIRC2025 Challenge, establishing the first public evaluation platform dedicated to infrared point tracking in surgical scenarios. Built upon the newly released Surgical Tattoo Infrared (STIR) dataset, the benchmark systematically evaluates algorithmic performance across both in vivo and ex vivo sequences, emphasizing tracking accuracy and inference efficiency. The challenge attracted seven participating teams and provided standardized data, evaluation metrics, and a low-latency testing framework. This effort successfully established a reproducible benchmark, significantly advancing the development of surgical visual perception algorithms.

0 citationsRead paper

Track2Map: Online Deformable SLAM with Motion-Aware Pose Optimization in Robotic Surgery

Jul 09, 2026

This work addresses the limitations of existing Gaussian splatting methods in robot-assisted minimally invasive surgery, which rely on offline processing and accurate prior camera trajectories, rendering them ineffective under missing priors or noisy conditions. The paper proposes an online 3D Gaussian splatting SLAM framework that jointly optimizes camera poses and deformable anatomical structures from monocular or stereo surgical videos, enabling real-time reconstruction without dependable trajectory priors. Key innovations include deformation initialization from dense 2D point trajectories, statistical trajectory analysis to disentangle camera motion from tissue deformation for drift suppression, and a static-camera interval detection mechanism. Evaluated on the StereoMIS dataset, the method outperforms both existing SLAM approaches and non-SLAM methods that depend on trajectory priors in terms of reconstruction fidelity and trajectory accuracy.

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SCARED-C: Corrected Camera Poses for Endoscopic Depth Estimation

May 15, 2026

This work addresses significant depth errors in the original SCARED dataset, where non-keyframe camera poses were estimated using robot kinematics, yielding only 35 reliable keyframes. To overcome this limitation, the authors employ COLMAP for structure-from-motion (SfM) to re-estimate camera poses for all frames and align the resulting reconstruction to the ground-truth depth of the original keyframes for metric scale recovery. This process produces a high-fidelity endoscopic RGB-D dataset, expanding the number of reliable RGB-D samples from 35 to 17,135 and substantially enhancing data usability. The refined dataset demonstrates superior performance in both stereo matching and monocular depth estimation tasks and is publicly released alongside the correction pipeline and code.

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