EndoGSim: Physics-Aware 4D Dynamic Endoscopic Scene Simulations via MLLM-Guided Gaussian Splatting
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910223819603968 |
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| author | Liu, Changjing Huang, Yiming Bai, Long Cui, Beilei Ren, Hongliang |
| author_facet | Liu, Changjing Huang, Yiming Bai, Long Cui, Beilei Ren, Hongliang |
| contents | In robot-assisted minimally invasive surgery, high-fidelity dynamic endoscopic scene reconstruction and simulation are crucial to enhancing downstream tasks and advancing surgical outcomes. However, existing methods primarily focus on visual reconstruction, lacking physics-based descriptions of the scene required for realistic simulation. We propose a unified framework that achieves physics-aware reconstruction and physical simulation of endoscopic scenes through Multi-modal Large Language Models (MLLMs)-guided Gaussian Splatting. Our approach utilizes 4D Gaussian Splatting (4DGS) integrated with pre-trained segmentation and depth estimation to represent deformable tissues and tools. To achieve automatic inference of physical properties, we introduce an object-wise material field that initializes material parameters via MLLM and refines them through a differentiable Material Point Method (MPM) under joint supervision from rendered images and optical flow. Validated on both open-source and in-house datasets, our framework achieves superior simulation fidelity and physical accuracy compared to state-of-the-art methods, underscoring its potential to advance robot-assisted surgical applications. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_16022 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | EndoGSim: Physics-Aware 4D Dynamic Endoscopic Scene Simulations via MLLM-Guided Gaussian Splatting Liu, Changjing Huang, Yiming Bai, Long Cui, Beilei Ren, Hongliang Computer Vision and Pattern Recognition In robot-assisted minimally invasive surgery, high-fidelity dynamic endoscopic scene reconstruction and simulation are crucial to enhancing downstream tasks and advancing surgical outcomes. However, existing methods primarily focus on visual reconstruction, lacking physics-based descriptions of the scene required for realistic simulation. We propose a unified framework that achieves physics-aware reconstruction and physical simulation of endoscopic scenes through Multi-modal Large Language Models (MLLMs)-guided Gaussian Splatting. Our approach utilizes 4D Gaussian Splatting (4DGS) integrated with pre-trained segmentation and depth estimation to represent deformable tissues and tools. To achieve automatic inference of physical properties, we introduce an object-wise material field that initializes material parameters via MLLM and refines them through a differentiable Material Point Method (MPM) under joint supervision from rendered images and optical flow. Validated on both open-source and in-house datasets, our framework achieves superior simulation fidelity and physical accuracy compared to state-of-the-art methods, underscoring its potential to advance robot-assisted surgical applications. |
| title | EndoGSim: Physics-Aware 4D Dynamic Endoscopic Scene Simulations via MLLM-Guided Gaussian Splatting |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.16022 |