仅用三张图实现高精度3D重建，解决稀疏视图下传统方法效果差的问题。
Sparse2DGS: Sparse-View Surface Reconstruction using 2D Gaussian Splatting with Dense Point Cloud
- 结合DUSt3R与COLMAP MVS生成稠密点云，优化2D高斯初始化
- 在DTU数据集上仅用三张图即完成精准3D形状重建
- 适合低视图数场景下的高质量3D重建，如移动端或受限采集
高斯点阵（Gaussian Splatting, GS）因其快速有效的新型视图合成能力受到关注，并已应用于多视角图像的3D重建，可实现快速准确的重建。然而，GS通常依赖大量多视角图像输入，当可用图像数量有限时，重建精度显著下降。主要原因在于通过运动恢复结构（SfM）获取的稀疏点云中3D点数量不足，导致高斯原型优化初始条件不佳。本文提出一种新方法Sparse2DGS，用于仅使用三张图像进行3D重建。该方法利用基于立体图像的基础模型DUSt3R和COLMAP MVS生成高度准确且稠密的3D点云，再将其用于初始化2D高斯。在DTU数据集上的实验表明，Sparse2DGS仅需三张图像即可准确重建物体的3D形状。
原文摘要 · Abstract (English)
Gaussian Splatting (GS) has gained attention as a fast and effective method for novel view synthesis. It has also been applied to 3D reconstruction using multi-view images and can achieve fast and accurate 3D reconstruction. However, GS assumes that the input contains a large number of multi-view images, and therefore, the reconstruction accuracy significantly decreases when only a limited number of input images are available. One of the main reasons is the insufficient number of 3D points in the sparse point cloud obtained through Structure from Motion (SfM), which results in a poor initialization for optimizing the Gaussian primitives. We propose a new 3D reconstruction method, called Sparse2DGS, to enhance 2DGS in reconstructing objects using only three images. Sparse2DGS employs DUSt3R, a fundamental model for stereo images, along with COLMAP MVS to generate highly accurate and dense 3D point clouds, which are then used to initialize 2D Gaussians. Through experiments on the DTU dataset, we show that Sparse2DGS can accurately reconstruct the 3D shapes of objects using just three images. The project page is available at https://gsisaoki.github.io/SPARSE2DGS/
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