Rendering high-fidelity images from sparse point clouds is still challenging. Existing learning-based approaches suffer from either hole artifacts, missing details, or expensive computations. In this paper, we propose a novel framework to render high-quality images from sparse points. This method first attempts to bridge the 3D Gaussian Splatting and point cloud rendering, which includes several cascaded modules. We first use a regressor to estimate Gaussian properties in a point-wise manner, the estimated properties are used to rasterize neural feature descriptors into 2D planes which are extracted from a multiscale extractor. The projected feature volume is gradually decoded toward the final prediction via a multiscale and progressive decoder. The whole pipeline experiences a two-stage training and is driven by our well-designed progressive and multiscale reconstruction loss. Experiments on different benchmarks show the superiority of our method in terms of rendering qualities and the necessities of our main components.

稀疏点云生成高保真图像仍具有挑战性，本文提出了一种从稀疏点集中生成高质量图像的新框架，通过多级模块实现了3D高斯点生成和点云渲染的融合，通过回归器和多尺度提取器将神经特征描述符投影为2D平面，然后逐步解码以实现最终预测，整个流程通过逐步和多尺度的重建损失进行两阶段训练。实验结果表明我们的方法在渲染质量上具有优越性，并验证了我们的核心组件的必要性。

PFGS：基于特征散放的高保真度点云渲染