# LGS: A Light-weight 4D Gaussian Splatting for Efficient Surgical Scene Reconstruction

arXiv: https://arxiv.org/abs/2406.16073 · HTML: https://mlsanity.com/paper/2406.16073/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 54 references that are in the map.
- First posted on arXiv 23 Jun 2024.

## Builds on (most cited first, 30 of 33)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [4D Gaussian Splatting for Real-Time Dynamic Scene Rendering](https://mlsanity.com/paper/2310.08528/) (2023)
- [LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS](https://mlsanity.com/paper/2311.17245/) (2023)
- [Compact 3D Gaussian Representation for Radiance Field](https://mlsanity.com/paper/2311.13681/) (2023)
- [Differentiable Surface Splatting for Point-based Geometry Processing](https://mlsanity.com/paper/1906.04173/) (2019)
- [U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation](https://mlsanity.com/paper/2406.02918/) (2024)
- [EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention](https://mlsanity.com/paper/2305.07027/) (2023)
- [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)
- [SIGMA: Semantic-complete Graph Matching for Domain Adaptive Object Detection](https://mlsanity.com/paper/2203.06398/) (2022)
- [EndoSurf: Neural Surface Reconstruction of Deformable Tissues with Stereo Endoscope Videos](https://mlsanity.com/paper/2307.11307/) (2023)
- [Endora: Video Generation Models as Endoscopy Simulators](https://mlsanity.com/paper/2403.11050/) (2024)
- [Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding](https://mlsanity.com/paper/2012.05440/) (2020)
- [SuPer: A Surgical Perception Framework for Endoscopic Tissue Manipulation with Surgical Robotics](https://mlsanity.com/paper/1909.05405/) (2019)
- [Neural LerPlane Representations for Fast 4D Reconstruction of Deformable Tissues](https://mlsanity.com/paper/2305.19906/) (2023)
- [EndoGS: Deformable Endoscopic Tissues Reconstruction with Gaussian Splatting](https://mlsanity.com/paper/2401.11535/) (2024)
- [StegaNeRF: Embedding Invisible Information within Neural Radiance Fields](https://mlsanity.com/paper/2212.01602/) (2022)
- [SurfelWarp: Efficient Non-Volumetric Single View Dynamic Reconstruction](https://mlsanity.com/paper/1904.13073/) (2019)
- [SuPer Deep: A Surgical Perception Framework for Robotic Tissue Manipulation using Deep Learning for Feature Extraction](https://mlsanity.com/paper/2003.03472/) (2020)
- [Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge](https://mlsanity.com/paper/2202.12031/) (2022)
- [LightNeuS: Neural Surface Reconstruction in Endoscopy using Illumination Decline](https://mlsanity.com/paper/2309.02777/) (2023)
- [Dynamic Reconstruction of Deformable Soft-tissue with Stereo Scope in Minimal Invasive Surgery](https://mlsanity.com/paper/2003.10867/) (2020)
- [Knowledge Condensation Distillation](https://mlsanity.com/paper/2207.05409/) (2022)
- [Learning to Estimate 6DoF Pose from Limited Data: A Few-Shot, Generalizable Approach using RGB Images](https://mlsanity.com/paper/2306.07598/) (2023)
- [BREAK: Bronchi Reconstruction by gEodesic transformation And sKeleton embedding](https://mlsanity.com/paper/2202.00002/) (2022)
- [ORBSLAM-based Endoscope Tracking and 3D Reconstruction](https://mlsanity.com/paper/1608.08149/) (2016)
- [Generator Versus Segmentor: Pseudo-healthy Synthesis](https://mlsanity.com/paper/2009.05722/) (2020)
- [AFSC: Adaptive Fourier Space Compression for Anomaly Detection](https://mlsanity.com/paper/2204.07963/) (2022)

## Built on by (most cited first, 21 of 21)

- [EndoSparse: Real-Time Sparse View Synthesis of Endoscopic Scenes using Gaussian Splatting](https://mlsanity.com/paper/2407.01029/) (2024)
- [VoxDet: Rethinking 3D Semantic Occupancy Prediction as Dense Object Detection](https://mlsanity.com/paper/2506.04623/) (2025)
- [Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting](https://mlsanity.com/paper/2506.12400/) (2025)
- [Dynamic Scene Reconstruction: Recent Advance in Real-time Rendering and Streaming](https://mlsanity.com/paper/2503.08166/) (2025)
- [GTP-4o: Modality-prompted Heterogeneous Graph Learning for Omni-modal Biomedical Representation](https://mlsanity.com/paper/2407.05540/) (2024)
- [A Review of 3D Reconstruction Techniques for Deformable Tissues in Robotic Surgery](https://mlsanity.com/paper/2408.04426/) (2024)
- [Multi-Layer Gaussian Splatting for Immersive Anatomy Visualization](https://mlsanity.com/paper/2410.16978/) (2024)
- [Deformable Gaussian Splatting for Efficient and High-Fidelity Reconstruction of Surgical Scenes](https://mlsanity.com/paper/2501.01101/) (2025)
- [When 3D Partial Points Meets SAM: Tooth Point Cloud Segmentation with Sparse Labels](https://mlsanity.com/paper/2409.01691/) (2024)
- [Instrument-Splatting: Controllable Photorealistic Reconstruction of Surgical Instruments Using Gaussian Splatting](https://mlsanity.com/paper/2503.04082/) (2025)
- [SpeeDe3DGS: Speedy Deformable 3D Gaussian Splatting with Temporal Pruning and Motion Grouping](https://mlsanity.com/paper/2506.07917/) (2025)
- [BridgeSplat: Bidirectionally Coupled CT and Non-Rigid Gaussian Splatting for Deformable Intraoperative Surgical Navigation](https://mlsanity.com/paper/2509.18501/) (2025)
- [ConcealGS: Concealing Invisible Copyright Information in 3D Gaussian Splatting](https://mlsanity.com/paper/2501.03605/) (2025)
- [Real-Time Spatio-Temporal Reconstruction of Dynamic Endoscopic Scenes with 4D Gaussian Splatting](https://mlsanity.com/paper/2411.01218/) (2024)
- [Moving Light Adaptive Colonoscopy Reconstruction via Illumination-Attenuation-Aware 3D Gaussian Splatting](https://mlsanity.com/paper/2510.18739/) (2025)
- [4D Monocular Surgical Reconstruction under Arbitrary Camera Motions](https://mlsanity.com/paper/2602.17473/) (2026)
- [DaRePlane: Direction-aware Representations for Dynamic Scene Reconstruction](https://mlsanity.com/paper/2410.14169/) (2024)
- [Feature-EndoGaussian: Feature Distilled Gaussian Splatting in Surgical Deformable Scene Reconstruction](https://mlsanity.com/paper/2503.06161/) (2025)
- [MetaScope: Optics-Driven Neural Network for Ultra-Micro Metalens Endoscopy](https://mlsanity.com/paper/2508.03596/) (2025)
- [Effective Gaussian Management for High-fidelity Scene Reconstruction](https://mlsanity.com/paper/2509.12742/) (2025)
- [Monocular Endoscopic Tissue 3D Reconstruction with Multi-Level Geometry Regularization](https://mlsanity.com/paper/2602.20718/) (2026)

## Abstract (arXiv)

The advent of 3D Gaussian Splatting (3D-GS) techniques and their dynamic scene modeling variants, 4D-GS, offers promising prospects for real-time rendering of dynamic surgical scenarios. However, the prerequisite for modeling dynamic scenes by a large number of Gaussian units, the high-dimensional Gaussian attributes and the high-resolution deformation fields, all lead to serve storage issues that hinder real-time rendering in resource-limited surgical equipment. To surmount these limitations, we introduce a Lightweight 4D Gaussian Splatting framework (LGS) that can liberate the efficiency bottlenecks of both rendering and storage for dynamic endoscopic reconstruction. Specifically, to minimize the redundancy of Gaussian quantities, we propose Deformation-Aware Pruning by gauging the impact of each Gaussian on deformation. Concurrently, to reduce the redundancy of Gaussian attributes, we simplify the representation of textures and lighting in non-crucial areas by pruning the dimensions of Gaussian attributes. We further resolve the feature field redundancy caused by the high resolution of 4D neural spatiotemporal encoder for modeling dynamic scenes via a 4D feature field condensation. Experiments on public benchmarks demonstrate efficacy of LGS in terms of a compression rate exceeding 9 times while maintaining the pleasing visual quality and real-time rendering efficiency. LGS confirms a substantial step towards its application in robotic surgical services.