突破3D高斯SLAM内存瓶颈，实现大规模场景实时重建。
DiskChunGS: Large-Scale 3D Gaussian SLAM Through Chunk-Based Memory Management
- 将场景分块存储，只在显存中保留活跃区域，其余存于硬盘。
- 在KITTI全部11个序列上无内存崩溃，重建质量优于现有方法。
- 适合需要大范围建图的自动驾驶与机器人应用。
3D高斯泼溅（3DGS）在新视角合成方面取得了显著成果，并具备实时渲染能力。然而，将其与SLAM系统结合时面临根本性可扩展性限制：受GPU内存容量制约，仅能重建小规模环境。我们提出DiskChunGS，一种通过分块内存管理实现的可扩展3DGS SLAM系统，采用外存式方法将场景划分为空间块，仅将活动区域保留在GPU内存中，非活动区域则存储在硬盘上。该架构可无缝集成现有SLAM框架以进行位姿估计和回环检测，支持全局一致的大规模重建。我们在室内场景（Replica、TUM-RGBD）、城市驾驶场景（KITTI）以及资源受限的Nvidia Jetson平台上验证了该方法。本方法在全部11个KITTI序列上均未发生内存失败，同时实现了更优的视觉质量，证明算法创新可突破以往3DGS SLAM因内存限制而无法拓展的瓶颈。
原文摘要 · Abstract (English)
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.
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