# Diff2DGS: Reliable Reconstruction of Occluded Surgical Scenes via 2D Gaussian Splatting

arXiv: https://arxiv.org/abs/2602.18314 · HTML: https://mlsanity.com/paper/2602.18314/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 40 references that are in the map.
- First posted on arXiv 20 Feb 2026.

## Builds on (most cited first, 30 of 30)

- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [4D Gaussian Splatting for Real-Time Dynamic Scene Rendering](https://mlsanity.com/paper/2310.08528/) (2023)
- [2D Gaussian Splatting for Geometrically Accurate Radiance Fields](https://mlsanity.com/paper/2403.17888/) (2024)
- [Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction](https://mlsanity.com/paper/2309.13101/) (2023)
- [HexPlane: A Fast Representation for Dynamic Scenes](https://mlsanity.com/paper/2301.09632/) (2023)
- [Spacetime Gaussian Feature Splatting for Real-Time Dynamic View Synthesis](https://mlsanity.com/paper/2312.16812/) (2023)
- [Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2011.02910/) (2020)
- [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)
- [Phased Consistency Models](https://mlsanity.com/paper/2405.18407/) (2024)
- [AVID: Any-Length Video Inpainting with Diffusion Model](https://mlsanity.com/paper/2312.03816/) (2023)
- [DiffuEraser: A Diffusion Model for Video Inpainting](https://mlsanity.com/paper/2501.10018/) (2025)
- [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)
- [VolumeFusion: Deep Depth Fusion for 3D Scene Reconstruction](https://mlsanity.com/paper/2108.08623/) (2021)
- [Neural LerPlane Representations for Fast 4D Reconstruction of Deformable Tissues](https://mlsanity.com/paper/2305.19906/) (2023)
- [Deform3DGS: Flexible Deformation for Fast Surgical Scene Reconstruction with Gaussian Splatting](https://mlsanity.com/paper/2405.17835/) (2024)
- [Towards Language-Driven Video Inpainting via Multimodal Large Language Models](https://mlsanity.com/paper/2401.10226/) (2024)
- [Reconstructing Sinus Anatomy from Endoscopic Video -- Towards a Radiation-free Approach for Quantitative Longitudinal Assessment](https://mlsanity.com/paper/2003.08502/) (2020)
- [Dynamic Reconstruction of Deformable Soft-tissue with Stereo Scope in Minimal Invasive Surgery](https://mlsanity.com/paper/2003.10867/) (2020)
- [EndoGaussian: Real-time Gaussian Splatting for Dynamic Endoscopic Scene Reconstruction](https://mlsanity.com/paper/2401.12561/) (2024)
- [Video Diffusion Models are Strong Video Inpainter](https://mlsanity.com/paper/2408.11402/) (2024)
- [Real-time Dense Reconstruction of Tissue Surface from Stereo Optical Video](https://mlsanity.com/paper/2007.12623/) (2020)
- [SurgicalGS: Dynamic 3D Gaussian Splatting for Accurate Robotic-Assisted Surgical Scene Reconstruction](https://mlsanity.com/paper/2410.09292/) (2024)
- [Automatic registration with continuous pose updates for marker-less surgical navigation in spine surgery](https://mlsanity.com/paper/2308.02917/) (2023)
- [Causal-Story: Local Causal Attention Utilizing Parameter-Efficient Tuning For Visual Story Synthesis](https://mlsanity.com/paper/2309.09553/) (2023)
- [Efficient EndoNeRF Reconstruction and Its Application for Data-driven Surgical Simulation](https://mlsanity.com/paper/2404.15339/) (2024)

## Built on by (most cited first, 2 of 2)

- [Track2Map: Online Deformable SLAM with Motion-Aware Pose Optimization in Robotic Surgery](https://mlsanity.com/paper/2607.08408/) (2026)
- [MV-dVRK: A Multi-Viewpoint Benchmark for Spatial Surgical Perception](https://mlsanity.com/paper/2609.02717/) (2026)

## Abstract (arXiv)

Real-time reconstruction of deformable surgical scenes is vital for advancing robotic surgery, improving intraoperative guidance, and enabling automation. Recent methods achieve dense reconstructions from da Vinci robotic surgery videos, with Gaussian Splatting (GS) offering real-time performance via graphics acceleration. However, reconstruction quality in occluded regions remains limited, and depth accuracy has not been fully assessed, as benchmarks like EndoNeRF and StereoMIS lack 3D ground truth. We propose Diff2DGS, a two-stage framework for reliable 3D reconstruction of occluded surgical scenes. First, a diffusion-based video module with temporal priors inpaints tissue occluded by instruments with high spatiotemporal consistency. Second, we adapt 2D Gaussian Splatting (2DGS) with a Learnable Deformation Model (LDM) to capture dynamic tissue deformation and anatomical geometry, and introduce adaptive depth weight to improve geometric fidelity. We further extend evaluation beyond image-quality metrics by performing quantitative depth analysis on the SCARED dataset. Diff2DGS outperforms state-of-the-art methods in both appearance and geometry, reaching 38.02 dB PSNR on EndoNeRF and 34.40 dB on StereoMIS. Our experiments also show that optimizing image quality alone does not necessarily ensure accurate 3D geometry.The code is available at https://diff2dgs.github.io/.