用2D高斯模型直接渲染稀疏点云，无需额外优化，效果更优。
Sparse Point Cloud Patches Rendering via Splitting 2D Gaussians
- 从点云直接预测2D高斯，通过分裂解码器提升精度
- 在多个数据集上达到当前最优性能，支持跨类别泛化
- 无需后处理，适合需要高效渲染的场景
现有基于学习的方法依赖类别先验、稠密点云或额外优化来实现逼真渲染。为此，本文提出一种新方法：从点云直接预测2D高斯进行渲染。该方法采用两个相同模块与整体补丁架构，使网络可泛化至多数据集。模块利用点云的法向、颜色和距离信息对高斯进行归一化与初始化；随后通过分裂解码器复制并优化初始高斯，有效适应稀疏点云。训练完成后，方法可直接泛化到不同类别的点云，且预测的高斯可直接用于渲染，无需图像后处理，保留2D高斯的优势。在多个数据集上的实验表明，本方法性能优于现有方法，达到当前最优水平。
原文摘要 · Abstract (English)
Current learning-based methods predict NeRF or 3D Gaussians from point clouds to achieve photo-realistic rendering but still depend on categorical priors, dense point clouds, or additional refinements. Hence, we introduce a novel point cloud rendering method by predicting 2D Gaussians from point clouds. Our method incorporates two identical modules with an entire-patch architecture enabling the network to be generalized to multiple datasets. The module normalizes and initializes the Gaussians utilizing the point cloud information including normals, colors and distances. Then, splitting decoders are employed to refine the initial Gaussians by duplicating them and predicting more accurate results, making our methodology effectively accommodate sparse point clouds as well. Once trained, our approach exhibits direct generalization to point clouds across different categories. The predicted Gaussians are employed directly for rendering without additional refinement on the rendered images, retaining the benefits of 2D Gaussians. We conduct extensive experiments on various datasets, and the results demonstrate the superiority and generalization of our method, which achieves SOTA performance. The code is available at https://github.com/murcherful/GauPCRender}{https://github.com/murcherful/GauPCRender.
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