IRIS：基于交点的射线隐式可编辑场景
IRIS: Intersection-aware Ray-based Implicit Editable Scenes
AI总结：
IRIS提出一种高效交互式场景编辑方法，通过分析采样策略和连续特征聚合机制，解决传统射线追踪的效率问题，实现高保真实时渲染和灵活形状编辑。
摘要：
Neural Radiance Fields 虽能实现高保真场景表示，但训练和渲染成本高；3D高斯散射具有实时性能但经验结果强。本文提出 IRIS 框架，通过高斯作为代理指导神经场评估，采用分析采样策略精确识别射线与场景原始体的交点，消除空空间处理。进一步引入连续特征聚合机制，沿射线直接操作，通过插值排序交点的潜在属性，避免昂贵的3D邻居查找，确保几何一致性，实现高保真实时渲染和灵活形状编辑。代码见 https://github.com/gwilczynski95/iris.
英文摘要：
Neural Radiance Fields achieve high-fidelity scene representation but suffer from costly training and rendering, while 3D Gaussian splatting offers real-time performance with strong empirical results. Recently, solutions that harness the best of both worlds by using Gaussians as proxies to guide neural field evaluations, still suffer from significant computational inefficiencies. They typically rely on stochastic volumetric sampling to aggregate features, which severely limits rendering performance. To address this issue, a novel framework named IRIS (Intersection-aware Ray-based Implicit Editable Scenes) is introduced as a method designed for efficient and interactive scene editing. To overcome the limitations of standard ray marching, an analytical sampling strategy is employed that precisely identifies interaction points between rays and scene primitives, effectively eliminating empty space processing. Furthermore, to address the computational bottleneck of spatial neighbor lookups, a continuous feature aggregation mechanism is introduced that operates directly along the ray. By interpolating latent attributes from sorted intersections, costly 3D searches are bypassed, ensuring geometric consistency, enabling high-fidelity, real-time rendering, and flexible shape editing. Code can be found at https://github.com/gwilczynski95/iris.
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。
