让机器人扫描时更聪明，减少无效移动，提升重建质量。
TRACE: Ergodic Trajectory Optimization for Active Scene Reconstruction
- 将扫描路径设计为匹配场景信息分布的动态轨迹
- 在Replica数据集上提升1.5 dB的重建质量
- 适合需要高效三维重建的机器人导航任务
现有基于高斯点云的主动重建系统采用贪心策略，每步只优化单一最优观测视角，并通过短视距路径规划连接。这种贪心解耦忽略了全局场景结构，导致轨迹效率低，传感器在视点间移动时浪费大量感知能力。本文将主动重建建模为遍历性覆盖问题：传感器轨迹的时间平均空间统计应匹配当前地图诱导的目标信息分布。方法在线计算该目标分布，结合核遍历性时域规划器与梯度流、足迹衰减机制，实现映射与轨迹优化的闭环。在Replica数据集上对比传统下一最佳视点（NBV）基线，性能提升1.5 dB PSNR。代码已开源。
原文摘要 · Abstract (English)
Existing active reconstruction systems with Gaussian-splatting maps select observations greedily, optimizing a single next-best-view (NBV) at each step and connecting the chosen views by short-horizon path planning. This greedy decoupling disregards the global structure of scene information, producing inefficient trajectories that waste sensing capacity in transit between selected views. In this work, we study active reconstruction as an ergodic coverage problem: the time-averaged spatial statistics of the sensor trajectory should match a target information distribution induced by the current map. Our approach derives this target distribution online from uncertainty and visibility, and calculates ergodic trajectories via a kernel-ergodic horizon planner with gradient flow and footprint depletion, closing the loop between mapping and trajectory optimization. We thoroughly evaluate TRACE on the Replica dataset against the Next-Best-View (NBV) baselines, improving PSNR by 1.5 dB. Code: https://github.com/spikelab-jhu/trace-active-reconstruction.
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