StopThePop：面向视图一致性实时渲染的排序高斯溅射
StopThePop: Sorted Gaussian Splatting for View-Consistent Real-time Rendering
AI总结：
针对3D高斯溅射视角旋转时的跳变伪影与视图不一致问题，提出低开销分层光栅化方法，在保证渲染质量的同时实现比原方法快1.6倍、内存占用减半的效果。
摘要：
高斯溅射（Gaussian Splatting）已成为跨多领域从图像构建3D表征的主流模型。但3D高斯溅射渲染管线的效率依赖若干简化措施，其中尤为突出的是，通过单一视图空间深度将高斯体简化为2D溅射斑，会在视角旋转时产生跳变（popping）和混合伪影。解决该问题需要精确的逐像素深度计算，但完整的逐像素排序相比全局排序操作成本过高。本文提出一种新型分层光栅化方法，能够以极低的处理开销系统地对溅射斑进行重排序与剔除。我们的软件光栅化器可有效消除跳变伪影与视图不一致性，定量与定性测量结果均验证了这一点。同时，该方法减少了利用跳变伪造视图相关效果的可能，确保表征更具真实性。尽管消除了伪造效果，我们的方法在测试图像上取得了与原方法相当的定量结果，同时提升了运动状态下新视角合成的一致性。得益于设计特性，我们的分层方法平均仅比原始高斯溅射慢4%。值得注意的是，强制一致性可将高斯体数量减少约一半，同时保持几乎相同的质量与视图一致性，最终渲染性能几乎翻倍，使我们的方法比原始高斯溅射快1.6倍，内存需求降低50%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
