Recent advancements in 3D Gaussian Splatting (3DGS) have substantially improved novel view synthesis, enabling high-quality reconstruction and real-time rendering. However, blurring artifacts, such as floating primitives and over-reconstruction, remain challenging. Current methods address these issues by refining scene structure, enhancing geometric representations, addressing blur in training images, improving rendering consistency, and optimizing density control, yet the role of kernel design remains underexplored. We identify the soft boundaries of Gaussian ellipsoids as one of the causes of these artifacts, limiting detail capture in high-frequency regions. To bridge this gap, we introduce 3D Linear Splatting (3DLS), which replaces Gaussian kernels with linear kernels to achieve sharper and more precise results, particularly in high-frequency regions. Through evaluations on three datasets, 3DLS demonstrates state-of-the-art fidelity and accuracy, along with a 30% FPS improvement over baseline 3DGS. The implementation will be made publicly available upon acceptance.
近期在 3D Gaussian Splatting (3DGS) 上的进展显著提升了新视图合成的质量，实现了高质量重建和实时渲染。然而，模糊伪影（如漂浮基元和过度重建）依然是一个难题。目前的方法通过优化场景结构、增强几何表示、处理训练图像中的模糊、改进渲染一致性以及优化密度控制来解决这些问题，但内核设计的重要性却未被充分探索。 我们发现，高斯椭球的软边界是导致这些伪影的原因之一，限制了高频区域细节的捕获。为解决这一问题，我们提出 3D Linear Splatting (3DLS)，用线性内核替代高斯内核，在高频区域实现了更清晰、更精确的结果。 在三个数据集上的评估中，3DLS 展现了当前最先进的保真度和准确性，并在帧率上相比基准的 3DGS 提升了 30%。代码将在论文接收后公开。