# DynaSurfGS: Dynamic Surface Reconstruction with Planar-based Gaussian Splatting

arXiv: https://arxiv.org/abs/2408.13972 · HTML: https://mlsanity.com/paper/2408.13972/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 26 Aug 2024.

## Builds on (most cited first, 7 of 7)

- [Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis](https://mlsanity.com/paper/2308.09713/) (2023)
- [PGSR: Planar-based Gaussian Splatting for Efficient and High-Fidelity Surface Reconstruction](https://mlsanity.com/paper/2406.06521/) (2024)
- [HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields](https://mlsanity.com/paper/2106.13228/) (2021)
- [Dynamic Gaussians Mesh: Consistent Mesh Reconstruction from Dynamic Scenes](https://mlsanity.com/paper/2404.12379/) (2024)
- [ND-SDF: Learning Normal Deflection Fields for High-Fidelity Indoor Reconstruction](https://mlsanity.com/paper/2408.12598/) (2024)
- [D3FlowSLAM: Self-Supervised Dynamic SLAM with Flow Motion Decomposition and DINO Guidance](https://mlsanity.com/paper/2207.08794/) (2022)
- [MaGS: Reconstructing and Simulating Dynamic 3D Objects with Mesh-adsorbed Gaussian Splatting](https://mlsanity.com/paper/2406.01593/) (2024)

## Built on by (most cited first, 19 of 19)

- [DiffPano: Scalable and Consistent Text to Panorama Generation with Spherical Epipolar-Aware Diffusion](https://mlsanity.com/paper/2410.24203/) (2024)
- [PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation](https://mlsanity.com/paper/2411.14423/) (2024)
- [CityGS-X: A Scalable Architecture for Efficient and Geometrically Accurate Large-Scale Scene Reconstruction](https://mlsanity.com/paper/2503.23044/) (2025)
- [StreetSurfGS: Scalable Urban Street Surface Reconstruction with Planar-based Gaussian Splatting](https://mlsanity.com/paper/2410.04354/) (2024)
- [Reconstructing 4D Spatial Intelligence: A Survey](https://mlsanity.com/paper/2507.21045/) (2025)
- [GauSTAR: Gaussian Surface Tracking and Reconstruction](https://mlsanity.com/paper/2501.10283/) (2025)
- [Dynamic 2D Gaussians: Geometrically Accurate Radiance Fields for Dynamic Objects](https://mlsanity.com/paper/2409.14072/) (2024)
- [DGNS: Deformable Gaussian Splatting and Dynamic Neural Surface for Monocular Dynamic 3D Reconstruction](https://mlsanity.com/paper/2412.03910/) (2024)
- [ARAP-GS: Drag-driven As-Rigid-As-Possible 3D Gaussian Splatting Editing with Diffusion Prior](https://mlsanity.com/paper/2504.12788/) (2025)
- [PhysGaia: A Physics-Aware Benchmark with Multi-Body Interactions for Dynamic Novel View Synthesis](https://mlsanity.com/paper/2506.02794/) (2025)
- [CAGE-GS: High-fidelity Cage Based 3D Gaussian Splatting Deformation](https://mlsanity.com/paper/2504.12800/) (2025)
- [GS-I3: Gaussian Splatting for Surface Reconstruction from Illumination-Inconsistent Images](https://mlsanity.com/paper/2503.12335/) (2025)
- [GP-4DGS: Probabilistic 4D Gaussian Splatting from Monocular Video via Variational Gaussian Processes](https://mlsanity.com/paper/2604.02915/) (2026)
- [textit4DSurf: High-Fidelity Dynamic Scene Surface Reconstruction](https://mlsanity.com/paper/2603.28064/) (2026)
- [DySurface: Consistent 4D Surface Reconstruction via Bridging Explicit Gaussians and Implicit Functions](https://mlsanity.com/paper/2605.10360/) (2026)
- [Detailed Geometry and Appearance from Opportunistic Motion](https://mlsanity.com/paper/2603.26665/) (2026)
- [4D Vessel Reconstruction for Benchtop Thrombectomy Analysis](https://mlsanity.com/paper/2604.06671/) (2026)
- [Future Rendering neq Future Surface: A Benchmark and Dataset for Dynamic Surface Reconstruction Beyond the Observed Window](https://mlsanity.com/paper/2607.21471/) (2026)
- [ToCo-Mesh: Topology-Consistent Dynamic Mesh Reconstruction via Adaptive Tessellation and Surface-Aligned 2DGS](https://mlsanity.com/paper/2609.29529/) (2026)

## Abstract (arXiv)

Dynamic scene reconstruction has garnered significant attention in recent years due to its capabilities in high-quality and real-time rendering. Among various methodologies, constructing a 4D spatial-temporal representation, such as 4D-GS, has gained popularity for its high-quality rendered images. However, these methods often produce suboptimal surfaces, as the discrete 3D Gaussian point clouds fail to align with the object's surface precisely. To address this problem, we propose DynaSurfGS to achieve both photorealistic rendering and high-fidelity surface reconstruction of dynamic scenarios. Specifically, the DynaSurfGS framework first incorporates Gaussian features from 4D neural voxels with the planar-based Gaussian Splatting to facilitate precise surface reconstruction. It leverages normal regularization to enforce the smoothness of the surface of dynamic objects. It also incorporates the as-rigid-as-possible (ARAP) constraint to maintain the approximate rigidity of local neighborhoods of 3D Gaussians between timesteps and ensure that adjacent 3D Gaussians remain closely aligned throughout. Extensive experiments demonstrate that DynaSurfGS surpasses state-of-the-art methods in both high-fidelity surface reconstruction and photorealistic rendering.