ControlGS：面向部署感知的高斯泼溅的一致结构压缩控制
ControlGS: Consistent Structural Compression Control for Deployment-Aware Gaussian Splatting
AI总结：
ControlGS提出一种场景无关的连续控制优化框架，通过统一超参数灵活调节高斯数量与渲染质量的权衡，在相同或更少高斯数量下实现更高渲染质量，支持自动化部署。
摘要：
3D高斯泼溅（3DGS）是一种高度可部署的新视角合成实时方法。在实际应用中，它需要一种通用且一致的控制机制，能够在无需针对特定场景调优的情况下调节渲染质量与模型压缩之间的权衡，从而支持在不同设备性能和通信带宽条件下实现自动化部署。本文提出了ControlGS，这是一个面向控制的优化框架，将高斯数量与渲染质量之间的权衡映射到一个连续的、与场景无关且高度响应的控制轴上。在广泛的场景规模和类型（从小型物体到大型室外场景）上进行的大量实验表明，通过调整一个全局统一的控制超参数，ControlGS能够灵活地生成偏向结构紧凑性或高保真度的模型，无论具体场景的规模或复杂度如何，同时与潜在的竞争方法相比，在相同或更少高斯数量的情况下实现显著更高的渲染质量。项目主页：https://zhang-fengdi.github.io/ControlGS/
英文摘要：
3D Gaussian Splatting (3DGS) is a highly deployable real-time method for novel view synthesis. In practice, it requires a universal, consistent control mechanism that adjusts the trade-off between rendering quality and model compression without scene-specific tuning, enabling automated deployment across different device performances and communication bandwidths. In this work, we present ControlGS, a control-oriented optimization framework that maps the trade-off between Gaussian count and rendering quality to a continuous, scene-agnostic, and highly responsive control axis. Extensive experiments across a wide range of scene scales and types (from small objects to large outdoor scenes) demonstrate that, by adjusting a globally unified control hyperparameter, ControlGS can flexibly generate models biased toward either structural compactness or high fidelity, regardless of the specific scene scale or complexity, while achieving markedly higher rendering quality with the same or fewer Gaussians compared to potential competing methods. Project page: https://zhang-fengdi.github.io/ControlGS/
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。
