# Efficient 3D Scene Reconstruction and Simulation from Sparse Endoscopic Views

arXiv: https://arxiv.org/abs/2509.17027 · HTML: https://mlsanity.com/paper/2509.17027/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 23 references that are in the map.
- First posted on arXiv 21 Sep 2025.

## Builds on (most cited first, 21 of 21)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [DUSt3R: Geometric 3D Vision Made Easy](https://mlsanity.com/paper/2312.14132/) (2023)
- [VGGT: Visual Geometry Grounded Transformer](https://mlsanity.com/paper/2503.11651/) (2025)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [2D Gaussian Splatting for Geometrically Accurate Radiance Fields](https://mlsanity.com/paper/2403.17888/) (2024)
- [PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics](https://mlsanity.com/paper/2311.12198/) (2023)
- [SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenes](https://mlsanity.com/paper/2312.14937/) (2023)
- [Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments](https://mlsanity.com/paper/2301.04195/) (2023)
- [FSGS: Real-Time Few-shot View Synthesis using Gaussian Splatting](https://mlsanity.com/paper/2312.00451/) (2023)
- [VR-GS: A Physical Dynamics-Aware Interactive Gaussian Splatting System in Virtual Reality](https://mlsanity.com/paper/2401.16663/) (2024)
- [Neural Rendering for Stereo 3D Reconstruction of Deformable Tissues in Robotic Surgery](https://mlsanity.com/paper/2206.15255/) (2022)
- [Stereo Correspondence and Reconstruction of Endoscopic Data Challenge](https://mlsanity.com/paper/2101.01133/) (2021)
- [EndoSurf: Neural Surface Reconstruction of Deformable Tissues with Stereo Endoscope Videos](https://mlsanity.com/paper/2307.11307/) (2023)
- [Learning How To Robustly Estimate Camera Pose in Endoscopic Videos](https://mlsanity.com/paper/2304.08023/) (2023)
- [Neural LerPlane Representations for Fast 4D Reconstruction of Deformable Tissues](https://mlsanity.com/paper/2305.19906/) (2023)
- [Endo-4DGS: Endoscopic Monocular Scene Reconstruction with 4D Gaussian Splatting](https://mlsanity.com/paper/2401.16416/) (2024)
- [EndoGS: Deformable Endoscopic Tissues Reconstruction with Gaussian Splatting](https://mlsanity.com/paper/2401.11535/) (2024)
- [EndoGaussian: Real-time Gaussian Splatting for Dynamic Endoscopic Scene Reconstruction](https://mlsanity.com/paper/2401.12561/) (2024)
- [Efficient EndoNeRF Reconstruction and Its Application for Data-driven Surgical Simulation](https://mlsanity.com/paper/2404.15339/) (2024)
- [SimEndoGS: Efficient Data-driven Scene Simulation using Robotic Surgery Videos via Physics-embedded 3D Gaussians](https://mlsanity.com/paper/2405.00956/) (2024)

## Abstract (arXiv)

Surgical simulation is essential for medical training, enabling practitioners to develop crucial skills in a risk-free environment while improving patient safety and surgical outcomes. However, conventional methods for building simulation environments are cumbersome, time-consuming, and difficult to scale, often resulting in poor details and unrealistic simulations. In this paper, we propose a Gaussian Splatting-based framework to directly reconstruct interactive surgical scenes from endoscopic data while ensuring efficiency, rendering quality, and realism. A key challenge in this data-driven simulation paradigm is the restricted movement of endoscopic cameras, which limits viewpoint diversity. As a result, the Gaussian Splatting representation overfits specific perspectives, leading to reduced geometric accuracy. To address this issue, we introduce a novel virtual camera-based regularization method that adaptively samples virtual viewpoints around the scene and incorporates them into the optimization process to mitigate overfitting. An effective depth-based regularization is applied to both real and virtual views to further refine the scene geometry. To enable fast deformation simulation, we propose a sparse control node-based Material Point Method, which integrates physical properties into the reconstructed scene while significantly reducing computational costs. Experimental results on representative surgical data demonstrate that our method can efficiently reconstruct and simulate surgical scenes from sparse endoscopic views. Notably, our method takes only a few minutes to reconstruct the surgical scene and is able to produce physically plausible deformations in real-time with user-defined interactions.