让3D场景实时更新，只改变化部分，不重算全图
CL-Splats: Continual Learning of Gaussian Splatting with Local Optimization
- 用局部优化只更新变化区域，避免全局重算
- 在动态场景中比现有方法重建质量更高
- 适合机器人、虚拟现实等需持续更新的场景
在动态3D环境中，准确地随时间更新场景表征对机器人、混合现实和具身AI应用至关重要。随着场景演化，需要高效方法来融入变化，以维持高精度重建，同时避免重新优化整个场景的计算开销。本文提出CL-Splats，从稀疏场景捕获中增量式更新基于高斯点阵的3D表征。该方法集成一个鲁棒的变化检测模块，可分割场景中的变化与静态部分，实现聚焦的局部优化，避免不必要的重计算。此外，CL-Splats支持存储和恢复先前的场景状态，促进时间分割与新场景分析应用。大量实验表明，相比当前最优方法，CL-Splats在保持高效更新的同时实现了更优的重建质量，为未来实时3D场景重建任务中的自适应提供了坚实基础。
原文摘要 · Abstract (English)
In dynamic 3D environments, accurately updating scene representations over time is crucial for applications in robotics, mixed reality, and embodied AI. As scenes evolve, efficient methods to incorporate changes are needed to maintain up-to-date, high-quality reconstructions without the computational overhead of re-optimizing the entire scene. This paper introduces CL-Splats, which incrementally updates Gaussian splatting-based 3D representations from sparse scene captures. CL-Splats integrates a robust change-detection module that segments updated and static components within the scene, enabling focused, local optimization that avoids unnecessary re-computation. Moreover, CL-Splats supports storing and recovering previous scene states, facilitating temporal segmentation and new scene-analysis applications. Our extensive experiments demonstrate that CL-Splats achieves efficient updates with improved reconstruction quality over the state-of-the-art. This establishes a robust foundation for future real-time adaptation in 3D scene reconstruction tasks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。