DynActiveGS：面向动态场景重建的主动高斯溅射方法
DynActiveGS: Active Gaussian Splatting for Dynamic Scene Reconstruction
- Tsinghua University, Shenzhen International Graduate School（清华大学深圳国际研究生院）
- Huawei Technologies Ltd（华为技术有限公司）
- Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））
- Peng Cheng Laboratory（鹏城实验室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
DynActiveGS是基于3DGS的动态感知主动重建框架，通过分解不确定性实现动态场景的鲁棒主动重建，在多指标上优于现有基线。
AI中文摘要：
我们提出了DynActiveGS，一种基于3D高斯溅射（3DGS）的动态感知主动重建框架，用于动态环境下的自主探索。该框架逐步重建3D高斯场景表示，同时通过在线不确定性预测和不确定性加权高斯优化抑制运动损坏的观测。DynActiveGS的关键组件是将不确定性显式分解为结构不确定性和运动诱导不确定性，这使系统能够区分未充分重建的静态区域与动态不可靠区域。基于这些不确定性场，DynActiveGS执行动态感知的视点选择和动态约束的路径规划，以在探索过程中偏好信息丰富且稳定的观测。由此形成的系统构成了用于动态场景中鲁棒主动重建的统一闭环管道。在具有挑战性的动态基准上进行的大量实验表明，与现有主动重建基线相比，该方法在重建精度、完整性、渲染质量和探索效率方面均实现了一致提升。
英文摘要：
We present DynActiveGS, a dynamic-aware active reconstruction framework based on 3D Gaussian Splatting (3DGS) for autonomous exploration in dynamic environments. The framework incrementally reconstructs a 3D Gaussian scene representation while suppressing motion-corrupted observations through online uncertainty prediction and uncertainty-weighted Gaussian optimization. A key component of DynActiveGS is the explicit decomposition of uncertainty into structural uncertainty and motion-induced uncertainty, which enables the system to distinguish under-reconstructed static regions from dynamically unreliable areas. Based on these uncertainty fields, DynActiveGS performs dynamic-aware viewpoint selection and dynamic-constrained path planning to favor informative yet stable observations during exploration. The resulting system forms a unified closed-loop pipeline for robust active reconstruction in dynamic scenes. Extensive experiments on challenging dynamic benchmarks demonstrate consistent improvements over existing active reconstruction baselines in reconstruction accuracy, completeness, rendering quality, and exploration efficiency.
