Recent advances in 3D Gaussian Splatting (3DGS) have demonstrated impressive results for novel view synthesis with real-time rendering capabilities. However, integrating 3DGS with SLAM systems faces a fundamental scalability limitation: methods are constrained by GPU memory capacity, restricting reconstruction to small-scale environments. We present DiskChunGS, a scalable 3DGS SLAM system that overcomes this bottleneck through an out-of-core approach that partitions scenes into spatial chunks and maintains only active regions in GPU memory while storing inactive areas on disk. Our architecture integrates seamlessly with existing SLAM frameworks for pose estimation and loop closure, enabling globally consistent reconstruction at scale. We validate DiskChunGS on indoor scenes (Replica, TUM-RGBD), urban driving scenarios (KITTI), and resource-constrained Nvidia Jetson platforms. Our method uniquely completes all 11 KITTI sequences without memory failures while achieving superior visual quality, demonstrating that algorithmic innovation can overcome the memory constraints that have limited previous 3DGS SLAM methods.

翻译：近年来，三维高斯泼溅（3DGS）在新视角合成与实时渲染方面取得了显著成果。然而，将3DGS与SLAM系统结合时面临根本性的可扩展性限制：现有方法受限于GPU显存容量，仅能重建小规模环境。我们提出DiskChunGS，一种可扩展的3DGS SLAM系统，通过基于外存的方法突破这一瓶颈——将场景划分为空间分块，仅在GPU内存中维护活跃区域，并将非活跃区域存储于磁盘。该架构可无缝集成至现有SLAM框架（如位姿估计与闭环检测），实现大规模全局一致性重建。我们在室内场景（Replica、TUM-RGBD）、城区驾驶场景（KITTI）及资源受限的Nvidia Jetson平台上验证了DiskChunGS性能。本方法独特地完成了所有11组KITTI序列的SLAM任务且无内存故障，同时实现了更优的视觉质量，证明算法创新可突破此前限制3DGS SLAM方法的内存约束。