AI 中文总结
本研究针对现有主动重建系统的贪婪解耦缺陷，提出TRACE遍历轨迹优化方法，在Replica数据集上较NBV基准将PSNR提升1.5 dB，实现更高效的主动场景重建。
AI 中文摘要
现有采用高斯溅射地图的主动重建系统，每一步都会贪婪地选择观测值，优化单个下一个最佳视图（NBV），并通过短视线路径规划连接所选视图。这种贪婪解耦忽略了场景信息的全局结构，产生低效轨迹，浪费所选视图间的传感容量。本研究将主动重建视为遍历覆盖问题：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。我们从不确定性和可见性在线推导该目标分布，通过带梯度流和足迹损耗的核遍历地平线规划器计算遍历轨迹，闭合建图与轨迹优化的循环。我们在Replica数据集上针对下一个最佳视图（NBV）基准全面评估TRACE，将峰值信噪比（PSNR）提升1.5 dB，代码可访问指定链接。
英文摘要
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.
Comments11 pages, 7 figures, fixed a template bug in the Latex
