GaussAnything：面向独立VR的语义意图驱动的演化高斯场景精化
GaussAnything: Semantic Intent-Driven Refinement of Evolving Gaussian Scenes for Standalone VR
- Intelligent Space Robotics Laboratory, Skolkovo Institute of Science and Technology（斯科尔科沃科学技术学院智能空间机器人实验室）
- NLP Research Center（自然语言处理研究中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对独立VR资源受限问题，提出GaussAnything系统，通过语义意图驱动的高斯预算重分配与渐进发布，实现高效高质量的场景渲染，显著提升查询对象细节。
AI中文摘要：
在独立VR头显上部署重建的3D环境受限于有限的计算和内存，传统的细节层次策略优化可见性而未考虑用户的明确检查意图。我们提出GaussAnything，一个原生的OpenXR系统，用于意图条件的内存重分配和演化语义高斯+SDF场景的渐进式发布。GaussAnything将类级或实例级查询解析为持久的3D对象，并将固定的高斯驻留预算重新分配给所选对象，同时保留全局上下文，通过源纪元机制与TSDF导出的网格协调，应用增量的、稳定身份的更新。在八个场景中，对象查询将固定客户端预算的88-90%集中到查询对象上而不扩大其范围，设备端渲染以微小差距（高达36.7 dB）重现主机渲染，独立客户端在标准独立面板的帧预算内以约10毫秒的稳态GPU成本渲染每个立体帧。
英文摘要：
Deploying reconstructed 3D environments on standalone VR headsets are constrained by limited compute and memory, and conventional level-of-detail policies optimize for visibility without accounting for the user's explicit inspection intent. We present GaussAnything, a native OpenXR system for intent-conditioned reallocation and progressive publication of evolving semantic Gaussian+SDF scenes. GaussAnything resolves class- or instance-level queries to persistent 3D objects and reallocates a fixed Gaussian resident budget toward the selected object while retaining global context, applying incremental, stable-identity updates coordinated with the TSDF-derived mesh through a source-epoch mechanism. Across eight scenes, an object query concentrates 88-90% of the fixed client budget onto the queried object without enlarging it, on-device rendering reproduces the host render to within a small margin (up to 36.7 dB), and the standalone client renders each stereo frame at a steady-state GPU cost of roughly 10 ms within the frame budget of standard standalone panels.
