让3D高斯点云持续更新，适应随时间变化的动态场景。
Gaussian Mapping for Evolving Scenes
- 引入关键帧管理机制，自动淘汰过时观测数据。
- 在真实与合成数据上提升29.7%的图像质量，深度误差降低至1/3。
- 适合需要长期感知变化的AR、自动驾驶等场景。
具备新视角合成能力的映射系统，如3D高斯点云（3DGS），广泛应用于计算机视觉及增强现实、机器人和自动驾驶等领域。然而，多数现有方法仅限于静态场景。尽管近期工作已开始处理相机视域内的短时动态，但场景在视野外的长期演化仍鲜有研究。为此，我们提出一种动态场景自适应机制，持续更新3DGS以反映最新变化。由于过时观测会破坏重建一致性，我们进一步设计了一种新型关键帧管理机制，剔除陈旧数据的同时尽可能保留有效信息。我们在合成与真实世界数据集上对提出的动态场景映射系统（GaME）进行了全面评估，相较于最先进基线，在PSNR上提升29.7%，L1深度误差降低为原来的1/3。
原文摘要 · Abstract (English)
Mapping systems with novel view synthesis (NVS) capabilities, most notably 3D Gaussian Splatting (3DGS), are widely used in computer vision, as well as in various applications, including augmented reality, robotics, and autonomous driving. 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 to continuously update 3DGS to reflect the latest changes. Since maintaining consistency remains challenging due to stale observations disrupting the reconstruction process, we further propose a novel keyframe management mechanism that discards outdated observations while preserving as much information as possible.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.
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