AI 中文总结
EliGSiR提出一种有界计算下的连续RGB-D高斯建图方法，通过自适应视图调度、负载调节保真度和目标几何增长，在多个数据集上超越现有方法。
AI 中文摘要
传统3D高斯泼溅假设观测集是封闭的，并采用长时间优化调度。相比之下，连续RGB-D建图面临新观测在线到达，同时必须保留先前重建区域的问题。我们提出EliGSiR（证据引导的负载自适应增量高斯泼溅与图像回放），一种连续高斯建图器，它控制随着重建演化如何利用可用优化预算。地图引导的视图调度根据地图当前状态过滤冗余的输入视图，并重新考虑保留的视图。负载自适应保真度根据当前建图负载调整监督分辨率，而非遵循固定分辨率调度。目标几何增长将深度监督与高斯创建分离，并仅在重复的RGB-D观测指示缺失或错位结构的位置增加几何容量。这些机制共同调整哪些视图被优化、使用多少图像细节以及在建图活动期间表示在哪里增长。我们在Replica、TUM RGB-D、ScanNet++以及真实RGB-D传感器序列上评估EliGSiR，同时考虑最终重建和采集过程中可用的地图。在TUM RGB-D fr3/long_office_household上，EliGSiR使用与受控基线相同的地面真值建图位姿达到21.52 dB，而SplaTAM为19.42 dB。在跟踪位姿比较中，EliGSiR使用实时ORB-SLAM3位姿在155.5秒内达到23.02 dB，而CaRtGS使用其原生跟踪器在230.9秒内达到20.10 dB。我们进一步评估整个采集过程中的重建，并展示EliGSiR的自适应视图调度、监督保真度和几何增长如何改善可用建图预算的利用。
英文摘要
Conventional 3D Gaussian Splatting assumes a closed set of observations and long optimization schedules. Continual RGB-D mapping in contrast poses the problem that new observations arrive online, while previously reconstructed regions must be preserved. We present EliGSiR (Evidence-guided Load-adaptive Incremental Gaussian Splatting with Image Replay), a continual Gaussian mapper that controls how the available optimization budget is used as the reconstruction evolves. Map-Guided View Scheduling filters redundant incoming views and reconsiders retained views according to the current state of the map. Load-Adaptive Fidelity adjusts supervision resolution to the current mapping load instead of following a fixed resolution schedule. Targeted Geometry Growth separates depth supervision from Gaussian creation and adds geometric capacity only where repeated RGB-D observations indicate missing or misplaced structure. Together, these mechanisms adapt which views are optimized, how much image detail is used, and where the representation grows while mapping remains active. We evaluate EliGSiR on Replica, TUM RGB-D, ScanNet++, and real RGB-D sensor sequences, considering both the final reconstruction and the map available throughout acquisition. On TUM RGB-D fr3/long_office_household, EliGSiR reaches 21.52 dB with the same ground-truth mapping poses used by the controlled baselines, compared with 19.42 dB for SplaTAM. In the tracked-pose comparison, EliGSiR with live ORB-SLAM3 poses reaches 23.02 dB in 155.5 s, compared with 20.10 dB in 230.9 s for CaRtGS using its native tracker. We further evaluate reconstruction throughout acquisition and show how EliGSiR adaptive view scheduling, supervision fidelity, and geometry growth improve the use of the available mapping budget.
Comments8 pages, 8 figures
