  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.
