SurgCUT3R: Surgical Scene-Aware Continuous Understanding of Temporal 3D Representation

• URL: http://arxiv.org/abs/2603.06971v1
• Date: Sat, 07 Mar 2026 01:13:18 GMT
• Title: SurgCUT3R: Surgical Scene-Aware Continuous Understanding of Temporal 3D Representation
• Abstract summary: SurgCUT3R is a systematic framework that adapts unified 3D reconstruction models to the surgical domain.<n>First, we develop a data generation pipeline that exploits public stereo surgical datasets.<n>Second, we propose a hybrid supervision strategy that couples our pseudo-ground-truth with geometric self-correction.<n>Third, we introduce a hierarchical inference framework that employs two specialized models to effectively accumulated pose drift over long surgical videos.
• Score: 20.17317715120012
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reconstructing surgical scenes from monocular endoscopic video is critical for advancing robotic-assisted surgery. However, the application of state-of-the-art general-purpose reconstruction models is constrained by two key challenges: the lack of supervised training data and performance degradation over long video sequences. To overcome these limitations, we propose SurgCUT3R, a systematic framework that adapts unified 3D reconstruction models to the surgical domain. Our contributions are threefold. First, we develop a data generation pipeline that exploits public stereo surgical datasets to produce large-scale, metric-scale pseudo-ground-truth depth maps, effectively bridging the data gap. Second, we propose a hybrid supervision strategy that couples our pseudo-ground-truth with geometric self-correction to enhance robustness against inherent data imperfections. Third, we introduce a hierarchical inference framework that employs two specialized models to effectively mitigate accumulated pose drift over long surgical videos: one for global stability and one for local accuracy. Experiments on the SCARED and StereoMIS datasets demonstrate that our method achieves a competitive balance between accuracy and efficiency, delivering near state-of-the-art but substantially faster pose estimation and offering a practical and effective solution for robust reconstruction in surgical environments. Project page: https://chumo-xu.github.io/SurgCUT3R-ICRA26/.

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