DiskChunGS: Large-Scale 3D Gaussian SLAM Through Chunk-Based Memory Management

Abstract--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. 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. ECENT advances in neural representations for 3D scene reconstruction have revolutionized novel view synthesis, with 3D Gaussian Splatting (3DGS) [1] emerging as an exceptionally efficient and high-quality approach. Unlike volume-based methods [2]-[4] that struggle with rendering speed due to expensive ray marching, 3DGS provides real-time rendering capabilities while maintaining impressive visual fidelity.