Photo-realistic 3D Reconstruction is a fundamental problem in 3D computer vision. This domain has seen considerable advancements owing to the advent of recent neural rendering techniques. These techniques predominantly aim to focus on learning volumetric representations of 3D scenes and refining these representations via loss functions derived from rendering. Among these, 3D Gaussian Splatting (3D-GS) has emerged as a significant method, surpassing Neural Radiance Fields (NeRFs). 3D-GS uses parameterized 3D Gaussians for modeling both spatial locations and color information, combined with a tile-based fast rendering technique. Despite its superior rendering performance and speed, the use of 3D Gaussian kernels has inherent limitations in accurately representing discontinuous functions, notably at edges and corners for shape discontinuities, and across varying textures for color discontinuities. To address this problem, we propose to employ 3D Half-Gaussian (3D-HGS) kernels, which can be used as a plug-and-play kernel. Our experiments demonstrate their capability to improve the performance of current 3D-GS related methods and achieve state-of-the-art rendering performance on various datasets without compromising rendering speed.
光真实3D重建是3D计算机视觉中的一个基本问题。由于最近神经渲染技术的出现，这一领域取得了显著进展。这些技术主要旨在学习3D场景的体积表示，并通过从渲染派生的损失函数来细化这些表示。在这些技术中，3D高斯涂抹（3D-GS）已成为一种重要的方法，超越了神经辐射场（NeRFs）。3D-GS使用参数化的3D高斯核对空间位置和颜色信息进行建模，并结合了基于瓦片的快速渲染技术。尽管其渲染性能和速度优越，但使用3D高斯核在准确表示不连续函数方面存在固有限制，特别是在形状不连续的边缘和角落以及颜色不连续的不同纹理之间。为了解决这个问题，我们建议使用3D半高斯（3D-HGS）核，这可以作为即插即用的核心。我们的实验表明，它们能够提高当前3D-GS相关方法的性能，并在不影响渲染速度的情况下在各种数据集上实现最先进的渲染性能。