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arXiv · 2409.14072

Dynamic 2D Gaussians: Geometrically Accurate Radiance Fields for Dynamic Objects

Shuai Zhang·Guanjun Wu·Zhoufeng Xie·Xinggang Wang·Bin Feng·Wenyu Liu
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Abstract

Reconstructing objects and extracting high-quality surfaces play a vital role in the real world. Current 4D representations show the ability to render high-quality novel views for dynamic objects, but cannot reconstruct high-quality meshes due to their implicit or geometrically inaccurate representations. In this paper, we propose a novel representation that can reconstruct accurate meshes from sparse image input, named Dynamic 2D Gaussians (D-2DGS). We adopt 2D Gaussians for basic geometry representation and use sparse-controlled points to capture the 2D Gaussian's deformation. By extracting the object mask from the rendered high-quality image and masking the rendered depth map, we remove floaters that are prone to occur during reconstruction and can extract high-quality dynamic mesh sequences of dynamic objects. Experiments demonstrate that our D-2DGS is outstanding in reconstructing detailed and smooth high-quality meshes from sparse inputs. The code is available at https://github.com/hustvl/Dynamic-2DGS.

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Shuai Zhang, Guanjun Wu, Zhoufeng Xie, Xinggang Wang, Bin Feng, Wenyu Liu. 2024-09-21. Dynamic 2D Gaussians: Geometrically Accurate Radiance Fields for Dynamic Objects. https://arxiv.org/abs/2409.14072

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