用2D高斯表示动态物体，从稀疏图像重建出高质量网格序列。
Dynamic 2D Gaussians: Geometrically Accurate Radiance Fields for Dynamic Objects
- 用2D高斯作为几何基础，通过稀疏点控制其形变。
- 在稀疏输入下仍能生成细节丰富、表面平滑的动态网格。
- 适合需要精确几何重建的动态场景建模任务。
真实世界中物体重建与高质量表面提取至关重要。现有4D表示虽能渲染高质量新视角，但因隐式或几何不准确，难以重建优质网格。本文提出动态2D高斯（D-2DGS），一种可从稀疏图像输入重建精确网格的新表示方法。采用2D高斯作为基础几何表达，利用稀疏控制点捕捉高斯的形变。通过从渲染的高质量图像中提取物体掩码，并对深度图进行掩码处理，有效去除重建过程中常见的噪声点，从而获得高质量的动态网格序列。实验表明，D-2DGS在稀疏输入条件下仍能出色重建细节丰富且表面平滑的网格。代码已开源：https://github.com/hustvl/Dynamic-2DGS。
原文摘要 · Abstract (English)
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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