稀疏视角下重建3D场景，用新点云初始化提升精度
Dense Point Clouds Matter: Dust-GS for Scene Reconstruction from Sparse Viewpoints
- 用自适应深度掩码融合策略优化点云初始化
- 仅需少量输入图像即达更优重建质量
- 适合低密度视角采集的3D重建任务
3D高斯点阵（3DGS）在场景合成与新视角生成任务中表现优异。传统方法依赖结构光（SfM）生成的点云初始化3D高斯原语，但在稀疏视角条件下，受限于初始点云质量与图像数量，性能显著下降。本文提出Dust-GS，一种专为稀疏视角设计的新框架。其核心是引入创新的点云初始化技术，即使在稀疏输入下仍保持有效性。通过融合自适应深度掩码的混合策略，提升了重建场景的精度与细节。在多个基准数据集上的大量实验表明，Dust-GS在稀疏视角场景中优于传统3DGS方法，以更少输入图像实现更高重建质量。
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) has demonstrated remarkable performance in scene synthesis and novel view synthesis tasks. Typically, the initialization of 3D Gaussian primitives relies on point clouds derived from Structure-from-Motion (SfM) methods. However, in scenarios requiring scene reconstruction from sparse viewpoints, the effectiveness of 3DGS is significantly constrained by the quality of these initial point clouds and the limited number of input images. In this study, we present Dust-GS, a novel framework specifically designed to overcome the limitations of 3DGS in sparse viewpoint conditions. Instead of relying solely on SfM, Dust-GS introduces an innovative point cloud initialization technique that remains effective even with sparse input data. Our approach leverages a hybrid strategy that integrates an adaptive depth-based masking technique, thereby enhancing the accuracy and detail of reconstructed scenes. Extensive experiments conducted on several benchmark datasets demonstrate that Dust-GS surpasses traditional 3DGS methods in scenarios with sparse viewpoints, achieving superior scene reconstruction quality with a reduced number of input images.
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