EasySplat通过视图自适应优化，提升3D高斯溅射的初始化与建模效率。
EasySplat: View-Adaptive Learning makes 3D Gaussian Splatting Easy
- 基于视图相似性分组，用大规模点云先验替代SfM初始化
- 采用邻近高斯椭球平均形状自适应分裂，提升稠密化效率
- 适合追求快速高质量3D重建的视觉研究者和开发者
3D高斯溅射（3DGS）技术已实现令人满意的三维场景表示。尽管性能优异，其仍受限于结构光测法（SfM）在获取准确场景初始化上的不足，或稠密化策略的低效问题。本文提出新型框架EasySplat，实现高质量3DGS建模。不同于传统使用SfM进行初始化，我们引入新方法释放大规模点云地图的优势。具体地，提出基于视图相似性的高效分组策略，并利用鲁棒点云先验获得高质量点云与相机位姿用于3D场景初始化。在获得可靠场景结构后，提出一种新颖的稠密化方法：根据邻近高斯椭球的平均形状，结合KNN方案自适应分裂高斯原始体。该方法有效克服初始化与优化瓶颈，实现高效且精确的3DGS建模。大量实验表明，EasySplat在处理新视角合成任务上优于当前最先进水平。
原文摘要 · Abstract (English)
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.
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