Duplex-GS：基于代理引导加权混合的实时与顺序无关高斯溅射
Duplex-GS: Proxy-Guided Weighted Blending for Real-Time Order-Independent Gaussian Splatting
AI总结：
针对3D高斯溅射依赖顺序alpha混合和视图自适应基数排序导致的高开销问题，提出Duplex-GS双层次框架，通过单元代理管理、单元搜索光栅化及OIT加权和渲染技术，实现照片级真实感与实时性能，较现有方法加速1.5-4倍且基数排序开销降52.2%-86.9%。
摘要：
3D高斯溅射（3DGS）的最新进展展现出卓越的渲染保真度和效率。然而，这些方法仍依赖计算成本高昂的顺序alpha混合操作，导致显著的开销，尤其在资源受限平台上。本文提出Duplex-GS，一个双层次框架，整合代理高斯表示与顺序无关渲染技术，在保持实时性能的同时实现照片级真实感结果。为减轻视图自适应基数排序带来的开销，我们引入单元代理用于局部高斯管理，并提出单元搜索光栅化以进一步加速。通过将我们的框架与顺序无关透明度（OIT）无缝结合，我们开发了一种受物理启发的加权和渲染技术，可同时消除“弹出”和“透明度”伪影，在准确性和效率上均取得显著提升。在多种真实世界数据集上的大量实验证明，我们的方法在多尺度训练视图和大规模环境等不同场景中具有鲁棒性。我们的结果验证了OIT渲染范式在高斯溅射中优势，实现高质量渲染，相比现有基于OIT的高斯溅射方法获得1.5至4倍的加速，且在不降低质量的情况下将基数排序开销减少52.2%至86.9%。
英文摘要：
Recent advances in 3D Gaussian Splatting (3DGS) have demonstrated remarkable rendering fidelity and efficiency. However, these methods still rely on computationally expensive sequential alpha-blending operations, resulting in significant overhead, particularly on resource-constrained platforms. In this paper, we propose Duplex-GS, a dual-hierarchy framework that integrates proxy Gaussian representations with order-independent rendering techniques to achieve photorealistic results while sustaining real-time performance. To mitigate the overhead caused by view-adaptive radix sort, we introduce cell proxies for local Gaussians management and propose cell search rasterization for further acceleration. By seamlessly combining our framework with Order-Independent Transparency (OIT), we develop a physically inspired weighted sum rendering technique that simultaneously eliminates "popping" and "transparency" artifacts, yielding substantial improvements in both accuracy and efficiency. Extensive experiments on a variety of real-world datasets demonstrate the robustness of our method across diverse scenarios, including multi-scale training views and large-scale environments. Our results validate the advantages of the OIT rendering paradigm in Gaussian Splatting, achieving high-quality rendering with an impressive 1.5 to 4 speedup over existing OIT based Gaussian Splatting approaches and 52.2% to 86.9% reduction of the radix sort overhead without quality degradation.
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。
