We introduce SurgFormer, a multiresolution gated transformer for data driven soft tissue simulation on volumetric meshes. High fidelity biomechanical solvers are often too costly for interactive use, so we train SurgFormer on solver generated data to predict nodewise displacement fields at near real time rates. SurgFormer builds a fixed mesh hierarchy and applies repeated multibranch blocks that combine local message passing, coarse global self attention, and pointwise feedforward updates, fused by learned per node, per channel gates to adaptively integrate local and long range information while remaining scalable on large meshes. For cut conditioned simulation, resection information is encoded as a learned cut embedding and provided as an additional input, enabling a unified model for both standard deformation prediction and topology altering cases. We also introduce two surgical simulation datasets generated under a unified protocol with XFEM based supervision: a cholecystectomy resection dataset and an appendectomy manipulation and resection dataset with cut and uncut cases. To our knowledge, this is the first learned volumetric surrogate setting to study XFEM supervised cut conditioned deformation within the same volumetric pipeline as standard deformation prediction. Across diverse baselines, SurgFormer achieves strong accuracy with favorable efficiency, making it a practical backbone for both tasks. {Code, data, and project page: \href{https://mint-vu.github.io/SurgFormer/}{available here}}

翻译：我们提出了SurgFormer，一种用于体网格上数据驱动软组织模拟的多分辨率门控Transformer。高保真生物力学求解器通常因计算成本过高而难以用于交互场景，因此我们基于求解器生成的数据训练SurgFormer，以近实时速率预测节点位移场。SurgFormer构建固定网格层次结构，并应用重复的多分支模块，这些模块结合了局部消息传递、粗粒度全局自注意力机制以及逐点前馈更新，通过学习的逐节点、逐通道门控进行融合，从而自适应地整合局部与长程信息，同时在大规模网格上保持可扩展性。针对切割条件模拟，切除信息被编码为可学习的切割嵌入向量并作为附加输入提供，使得模型能够统一处理标准形变预测与拓扑改变情况。我们还基于统一协议生成了两个采用XFEM监督的手术模拟数据集：胆囊切除数据集以及包含切割与非切割情况的阑尾切除操作与切除数据集。据我们所知，这是首个在标准形变预测的同一体网格流程中研究XFEM监督下切割条件形变的可学习体网格代理框架。在多种基线方法对比中，SurgFormer在保持优异效率的同时实现了高精度，成为适用于两项任务的实用骨干框架。{代码、数据及项目页面：\href{https://mint-vu.github.io/SurgFormer/}{可在此处获取}}