# SurfelWarp: Efficient Non-Volumetric Single View Dynamic Reconstruction

arXiv: https://arxiv.org/abs/1904.13073 · HTML: https://mlsanity.com/paper/1904.13073/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2019.

## Built on by (most cited first, 30 of 31)

- [SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenes](https://mlsanity.com/paper/2312.14937/) (2023)
- [Monocular Dynamic View Synthesis: A Reality Check](https://mlsanity.com/paper/2210.13445/) (2022)
- [MoSca: Dynamic Gaussian Fusion from Casual Videos via 4D Motion Scaffolds](https://mlsanity.com/paper/2405.17421/) (2024)
- [4DComplete: Non-Rigid Motion Estimation Beyond the Observable Surface](https://mlsanity.com/paper/2105.01905/) (2021)
- [Neural Rendering for Stereo 3D Reconstruction of Deformable Tissues in Robotic Surgery](https://mlsanity.com/paper/2206.15255/) (2022)
- [Lepard: Learning partial point cloud matching in rigid and deformable scenes](https://mlsanity.com/paper/2111.12591/) (2021)
- [EndoSurf: Neural Surface Reconstruction of Deformable Tissues with Stereo Endoscope Videos](https://mlsanity.com/paper/2307.11307/) (2023)
- [E-DSSR: Efficient Dynamic Surgical Scene Reconstruction with Transformer-based Stereoscopic Depth Perception](https://mlsanity.com/paper/2107.00229/) (2021)
- [Tracking and Mapping in Medical Computer Vision: A Review](https://mlsanity.com/paper/2310.11475/) (2023)
- [EndoGS: Deformable Endoscopic Tissues Reconstruction with Gaussian Splatting](https://mlsanity.com/paper/2401.11535/) (2024)
- [Recent Trends in 3D Reconstruction of General Non-Rigid Scenes](https://mlsanity.com/paper/2403.15064/) (2024)
- [EndoGaussian: Real-time Gaussian Splatting for Dynamic Endoscopic Scene Reconstruction](https://mlsanity.com/paper/2401.12561/) (2024)
- [LGS: A Light-weight 4D Gaussian Splatting for Efficient Surgical Scene Reconstruction](https://mlsanity.com/paper/2406.16073/) (2024)
- [Efficient Deformable Tissue Reconstruction via Orthogonal Neural Plane](https://mlsanity.com/paper/2312.15253/) (2023)
- [Semantic-SuPer: A Semantic-aware Surgical Perception Framework for Endoscopic Tissue Identification, Reconstruction, and Tracking](https://mlsanity.com/paper/2210.16674/) (2022)
- [SurgicalGS: Dynamic 3D Gaussian Splatting for Accurate Robotic-Assisted Surgical Scene Reconstruction](https://mlsanity.com/paper/2410.09292/) (2024)
- [Feature4X: Bridging Any Monocular Video to 4D Agentic AI with Versatile Gaussian Feature Fields](https://mlsanity.com/paper/2503.20776/) (2025)
- [4DTAM: Non-Rigid Tracking and Mapping via Dynamic Surface Gaussians](https://mlsanity.com/paper/2505.22859/) (2025)
- [ImMesh: An Immediate LiDAR Localization and Meshing Framework](https://mlsanity.com/paper/2301.05206/) (2023)
- [Learnable Infinite Taylor Gaussian for Dynamic View Rendering](https://mlsanity.com/paper/2412.04282/) (2024)
- [Class-agnostic Reconstruction of Dynamic Objects from Videos](https://mlsanity.com/paper/2112.02091/) (2021)
- [Space-time 2D Gaussian Splatting for Accurate Surface Reconstruction under Complex Dynamic Scenes](https://mlsanity.com/paper/2409.18852/) (2024)
- [Partial Wasserstein Adversarial Network for Non-rigid Point Set Registration](https://mlsanity.com/paper/2203.02227/) (2022)
- [Scene-level Tracking and Reconstruction without Object Priors](https://mlsanity.com/paper/2210.03815/) (2022)
- [Mono-STAR: Mono-camera Scene-level Tracking and Reconstruction](https://mlsanity.com/paper/2301.13244/) (2023)
- [Motion Blender Gaussian Splatting for Dynamic Scene Reconstruction](https://mlsanity.com/paper/2503.09040/) (2025)
- [SuPerPM: A Surgical Perception Framework Based on Deep Point Matching Learned from Physical Constrained Simulation Data](https://mlsanity.com/paper/2309.13863/) (2023)
- [HybridReg: Robust 3D Point Cloud Registration with Hybrid Motions](https://mlsanity.com/paper/2503.07019/) (2025)
- [PoseFusion2: Simultaneous Background Reconstruction and Human Shape Recovery in Real-time](https://mlsanity.com/paper/2108.00695/) (2021)
- [Feature-EndoGaussian: Feature Distilled Gaussian Splatting in Surgical Deformable Scene Reconstruction](https://mlsanity.com/paper/2503.06161/) (2025)

## Abstract (arXiv)

We contribute a dense SLAM system that takes a live stream of depth images as input and reconstructs non-rigid deforming scenes in real time, without templates or prior models. In contrast to existing approaches, we do not maintain any volumetric data structures, such as truncated signed distance function (TSDF) fields or deformation fields, which are performance and memory intensive. Our system works with a flat point (surfel) based representation of geometry, which can be directly acquired from commodity depth sensors. Standard graphics pipelines and general purpose GPU (GPGPU) computing are leveraged for all central operations: i.e., nearest neighbor maintenance, non-rigid deformation field estimation and fusion of depth measurements. Our pipeline inherently avoids expensive volumetric operations such as marching cubes, volumetric fusion and dense deformation field update, leading to significantly improved performance. Furthermore, the explicit and flexible surfel based geometry representation enables efficient tackling of topology changes and tracking failures, which makes our reconstructions consistent with updated depth observations. Our system allows robots to maintain a scene description with non-rigidly deformed objects that potentially enables interactions with dynamic working environments.