3D Gaussian Splatting (3DGS) techniques have achieved satisfactory 3D scene representation. Despite their impressive performance, they confront challenges due to the limitation of structure-from-motion (SfM) methods on acquiring accurate scene initialization, or the inefficiency of densification strategy. In this paper, we introduce a novel framework EasySplat to achieve high-quality 3DGS modeling. Instead of using SfM for scene initialization, we employ a novel method to release the power of large-scale pointmap approaches. Specifically, we propose an efficient grouping strategy based on view similarity, and use robust pointmap priors to obtain high-quality point clouds and camera poses for 3D scene initialization. After obtaining a reliable scene structure, we propose a novel densification approach that adaptively splits Gaussian primitives based on the average shape of neighboring Gaussian ellipsoids, utilizing KNN scheme. In this way, the proposed method tackles the limitation on initialization and optimization, leading to an efficient and accurate 3DGS modeling. Extensive experiments demonstrate that EasySplat outperforms the current state-of-the-art (SOTA) in handling novel view synthesis.
3D 高斯喷射（3DGS）技术在三维场景表示方面取得了令人满意的成果。尽管其表现出色，但仍面临结构光恢复（SfM）方法在获取准确场景初始化中的局限性或密集化策略低效的挑战。在本文中，我们提出了一种新颖的框架 EasySplat，以实现高质量的 3DGS 建模。与传统基于 SfM 的场景初始化方法不同，我们采用了一种新方法来释放大规模点云映射方法的潜力。具体而言，我们提出了一种基于视图相似性的高效分组策略，并利用鲁棒的点云先验获得高质量的点云和相机位姿，用于三维场景初始化。 在获得可靠的场景结构后，我们提出了一种新颖的密集化方法，基于邻近高斯椭球的平均形状，利用 KNN 方案自适应地分裂高斯基元。通过这种方式，所提出的方法解决了初始化和优化的局限性，从而实现了高效且准确的 3DGS 建模。大量实验表明，EasySplat 在处理新视图合成方面优于当前最先进技术（SOTA）。