Gaussian Splatting has emerged as a prominent model for constructing 3D representations from images across diverse domains. However, the efficiency of the 3D Gaussian Splatting rendering pipeline relies on several simplifications. Notably, reducing Gaussian to 2D splats with a single view-space depth introduces popping and blending artifacts during view rotation. Addressing this issue requires accurate per-pixel depth computation, yet a full per-pixel sort proves excessively costly compared to a global sort operation. In this paper, we present a novel hierarchical rasterization approach that systematically resorts and culls splats with minimal processing overhead. Our software rasterizer effectively eliminates popping artifacts and view inconsistencies, as demonstrated through both quantitative and qualitative measurements. Simultaneously, our method mitigates the potential for cheating view-dependent effects with popping, ensuring a more authentic representation. Despite the elimination of cheating, our approach achieves comparable quantitative results for test images, while increasing the consistency for novel view synthesis in motion. Due to its design, our hierarchical approach is only 4% slower on average than the original Gaussian Splatting. Notably, enforcing consistency enables a reduction in the number of Gaussians by approximately half with nearly identical quality and view-consistency. Consequently, rendering performance is nearly doubled, making our approach 1.6x faster than the original Gaussian Splatting, with a 50% reduction in memory requirements.
高斯散射已作为一种突出的模型出现，用于从跨多个领域的图像中构建3D表示。然而，3D高斯散射渲染管线的效率依赖于几种简化。尤其是，将高斯简化为具有单一视图空间深度的2D散射体，会在视图旋转过程中引入弹出和混合伪像。解决这一问题需要准确的每像素深度计算，然而，与全局排序操作相比，完整的每像素排序证明过于昂贵。在本文中，我们提出了一种新颖的分层光栅化方法，该方法系统地重新排序和剔除散射体，同时最小化处理开销。我们的软件光栅化器有效地消除了弹出伪像和视图不一致性，通过定量和定性测量都得到了证明。同时，我们的方法减少了利用弹出现象作弊的视图依赖效果的可能性，确保了更真实的表示。尽管消除了作弊，我们的方法在测试图像的定量结果上与原始高斯散射相当，同时在运动中的新视图合成的一致性上有所增加。由于其设计，我们的分层方法平均仅比原始高斯散射慢4%。值得注意的是，强制一致性使得高斯的数量大约减半，几乎不影响质量和视图一致性。因此，渲染性能几乎翻倍，使我们的方法比原始高斯散射快1.6倍，内存需求减少50%。