动态场景下主动重建新框架，能区分静态与动态区域。
DynActiveGS: Active Gaussian Splatting for Dynamic Scene Reconstruction
- 分离结构与运动不确定性，智能筛选可靠观测
- 动态感知视角选择，提升重建准确率与效率
- 适合自动驾驶等动态环境下的实时三维重建
我们提出DynActiveGS，一种基于3D高斯溅射（3DGS）的动态感知主动重建框架，适用于动态环境中的自主探索。该框架在增量重建3D高斯场景表示的同时，通过在线不确定性预测和不确定性加权优化，抑制运动引起的误观测。其核心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，从而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，系统实现动态感知的视角选择与动态约束路径规划，优先获取信息丰富且稳定的观测。最终形成一个统一的闭环重建流程，可在复杂动态基准测试中持续优于现有主动重建基线，在重建精度、完整性、渲染质量与探索效率上均有提升。
原文摘要 · Abstract (English)
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.
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