将可移动物体作为可复用资产，实现动态场景的高效维护。
CubifyGS: Object-Centric 3D Gaussian Splatting for Lifelong Dynamic Scene Maintenance
- 将物体视为可复用的高斯资产，通过检索与变换更新地图
- 相比基线方法，显著减少伪影且维护效率提升
- 适合需要长期维护动态环境的机器人系统
在刚性物体重新排列的长期场景建图中仍面临根本性挑战。尽管3D高斯点阵（3DGS）能实现高保真建模，但原始级更新常导致持续伪影且恢复缓慢。本文提出CubifyGS，一种基于物体级别的映射框架，将动态维护从被动重优化转变为主动资产管理。CubifyGS将可移动实例建模为可复用的高斯资产，检测物体出现与消失，并通过资产检索、刚性变换和显式剔除来更新地图，而非从头重建。为解决此类编辑后产生的几何空洞与局部光照不一致问题，进一步提出事件触发的自适应优化策略，聚焦计算于受影响区域。我们在新构建的高保真动态基准上验证了该方法，在物体重排设置下，相比代表性可复现基线，CubifyGS在伪影抑制与维护效率方面均有显著提升。
原文摘要 · Abstract (English)
Lifelong scene mapping under rigid object rearrangement remains a fundamental challenge in robotics. While 3D Gaussian Splatting (3DGS) enables high-fidelity modeling, primitive-level updates often cause persistent ghosting and slow recovery. We propose CubifyGS, an object-level mapping framework that shifts dynamic maintenance from passive re-optimization to active asset management. CubifyGS models movable instances as reusable Gaussian assets, detects object appearance and disappearance, and updates maps through asset retrieval, rigid transformation, and explicit pruning rather than reconstruction from scratch. To address geometric voids and local photometric mismatch after such edits, we further propose an event-triggered adaptive optimization strategy that focuses computation on affected regions. We validate our approach on a newly constructed high-fidelity dynamic benchmark, demonstrating that CubifyGS improves artifact suppression and maintenance efficiency over representative reproducible baselines in the evaluated object-rearrangement setting.
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