Abstract
3D Gaussian Splatting (3DGS) has demonstrated its advantages in achieving fast and high-quality rendering. As point clouds serve as a widely-used and easily accessible form of 3D representation, bridging the gap between point clouds and Gaussians becomes increasingly important. Recent studies have explored how to convert the colored points into Gaussians, but directly generating Gaussians from colorless 3D point clouds remains an unsolved challenge. In this paper, we propose GAP, a novel approach that gaussianizes raw point clouds into high-fidelity 3D Gaussians with text guidance. Our key idea is to design a multi-view optimization framework that leverages a depth-aware image diffusion model to synthesize consistent appearances across different viewpoints. To ensure geometric accuracy, we introduce a surface-anchoring mechanism that effectively constrains Gaussians to lie on the surfaces of 3D shapes during optimization. Furthermore, GAP incorporates a diffuse-based inpainting strategy that specifically targets at completing hard-to-observe regions. We evaluate GAP on the Point-to-Gaussian generation task across varying complexity levels, from synthetic point clouds to challenging real-world scans, and even large-scale scenes. Project Page: this https URL.
Abstract (translated)
3D高斯点绘（3DGS）已经展示了其实现快速且高质量渲染的优势。由于点云作为一种广泛应用且易于访问的三维表示形式，弥合点云与高斯分布之间的差距变得越来越重要。最近的研究探讨了如何将带颜色的点转换为高斯分布，但直接从无色的3D点云生成高斯分布仍然是一个未解决的问题。在本文中，我们提出了一种名为GAP的新方法，该方法可以利用文本引导将原始点云转化为高质量的三维高斯分布。我们的核心思想是设计一个多视角优化框架，该框架使用深度感知图像扩散模型来合成不同视图下的连贯外观。为了确保几何准确性，我们引入了表面锚定机制，在优化过程中有效限制高斯分布在3D形状表面上的位置。此外，GAP整合了一种基于扩散的修补策略，专门针对难以观察到的区域进行填充。 我们在不同程度复杂性上评估了GAP在点云至高斯分布生成任务上的表现，从合成的点云数据集到具有挑战性的现实世界扫描以及大规模场景都进行了测试。项目页面：[请在此处插入具体的URL链接]。
URL
https://arxiv.org/abs/2508.05631
