面向演化场景的高斯建图
Gaussian Mapping for Evolving Scenes
针对相机视野外场景长期演化（long-term dynamics）导致的观测陈旧问题，提出持续更新 3D 高斯表示并配合丢弃过期观测的关键帧管理机制，可直接复用于 SLAM/建图系统中处理场景变化与表示一致性维护。
原文依据 we propose a novel keyframe management mechanism that discards outdated observations while preserving as much information as possible
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论具备新视角合成（NVS）能力的建图系统被广泛应用于计算机视觉以及增强现实、机器人和自动驾驶等多种应用中。其中最值得注意的是，基于 3D 高斯泼溅的系统展现出很高的新视角合成性能；然而，当前许多方法仅限于静态场景。尽管近期工作已开始处理短期动态（相机视野内的运动），但长期动态（场景因视野之外的改变而演化）仍较少被探索。为克服这一局限，我们引入一种动态场景自适应机制，持续更新三维表示以反映最新变化。此外，由于陈旧观测会干扰重建过程，维持几何与语义一致性仍然具有挑战性，我们提出一种新颖的关键帧管理机制，在丢弃过期观测的同时尽可能多地保留信息。我们在合成数据集和真实世界数据集上对面向演化场景的高斯建图（GaME）进行了充分评估，相比最具竞争力的基线，峰值信噪比提升 29.7%，L1 深度误差改善为原来的 3 倍。
英文原文摘要 1192 字符
Mapping systems with novel view synthesis (NVS) capabilities are widely used in computer vision, as well as in various applications, including augmented reality, robotics, and autonomous driving. Most notably, 3D Gaussian Splatting-based systems show high NVS performance; however, many current approaches are limited to static scenes. While recent works have begun addressing short-term dynamics (motion within the camera's view), long-term dynamics (the scene evolving through changes out of view) remain less explored.To overcome this limitation, we introduce a dynamic scene adaptation mechanism that continuously updates the 3D representation to reflect the latest changes. In addition, since maintaining geometric and semantic consistency remains challenging due to stale observations disrupting the reconstruction process, we propose a novel keyframe management mechanism that discards outdated observations while preserving as much information as possible. We thoroughly evaluate Gaussian Mapping for Evolving Scenes (GaME) on both synthetic and real-world datasets, achieving a 29.7% improvement in PSNR and a 3 times improvement in L1 depth error over the most competitive baseline.