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DynaSurfGS: Dynamic Surface Reconstruction with Planar-based Gaussian Splatting
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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.
Forward citations
Cited by 6 Pith papers
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GP-4DGS: Probabilistic 4D Gaussian Splatting from Monocular Video via Variational Gaussian Processes
Variational Gaussian Processes with spatio-temporal kernels supply probabilistic deformation priors to 4DGS, improving sparse-view reconstruction while yielding calibrated motion uncertainty and temporal extrapolation.
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Future Rendering $\neq$ Future Surface: A Benchmark and Dataset for Dynamic Surface Reconstruction Beyond the Observed Window
FutureSurf, a new benchmark for held-out future surface reconstruction, shows deformation-MLP methods leave a 2-6.6× future-surface gap while rendering quality stays flat.
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GauSTAR: Gaussian Surface Tracking and Reconstruction
A Gaussian-on-mesh representation with adaptive unbinding and re-meshing achieves best-on-reported-sequences dynamic surface reconstruction, rendering, and tracking under topology changes.
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DGNS: Deformable Gaussian Splatting and Dynamic Neural Surface for Monocular Dynamic 3D Reconstruction
A hybrid of deformable Gaussian splatting and dynamic neural SDF achieves state-of-the-art 3D mesh accuracy from monocular video while keeping view synthesis competitive.
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AdaAnchor4D: Anchor-Conditioned Spatiotemporal Feature Aggregation for Monocular UAV 4D Reconstruction
Anchor-conditioned adaptive aggregation of shared spatiotemporal feature planes improves monocular UAV dynamic Gaussian reconstruction quality while keeping real-time rendering.
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PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation
A pipeline that infers object material with a multimodal model and optimizes material parameters with optical flow from video diffusion to simulate 4D dynamic scenes.
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