Core Insights - 3D Gaussian Splatting (3DGS) is an emerging method for novel view synthesis that utilizes a set of images with poses to iteratively train a scene representation composed of numerous anisotropic 3D Gaussian bodies, capturing the appearance and geometry of the scene [2][4] - The CLM system proposed by the team allows 3DGS to render large scenes using a single consumer-grade GPU, such as the RTX 4090, by addressing GPU memory limitations [6][8] Group 1: 3DGS Overview - 3DGS has shown revolutionary application potential in fields such as 3D modeling, digital twins, visual effects (VFX), VR/AR, and robot vision reconstruction (SLAM) [5] - The quality of images rendered using 3DGS depends on the fidelity of the trained scene representation, with larger and more complex scenes requiring more Gaussian bodies, leading to increased memory usage [5] Group 2: CLM System Design - CLM is designed based on the insight that the computation of 3DGS is inherently sparse, allowing only a small subset of Gaussian bodies to be accessed during each training iteration [8][20] - The system employs a novel unloading strategy that minimizes performance overhead and scales to large scenes by dynamically loading only the necessary Gaussian bodies into GPU memory while offloading the rest to CPU memory [8][11] Group 3: Performance and Efficiency - The implementation of CLM can render a large scene requiring 102 million Gaussian bodies on a single RTX 4090 while achieving top-tier reconstruction quality [8] - Each view typically accesses only 0.39% of the Gaussian points, with a maximum of 1.06% for any single view, highlighting the sparse nature of the data [23] Group 4: Optimization Techniques - The team utilized several unique characteristics of 3DGS to significantly reduce communication overhead associated with unloading, including pre-computing the accessed Gaussian sets for each view and leveraging spatial locality to optimize data transfer between CPU and GPU [12][17] - The microbatch scheduling optimization allows for overlapping access patterns between consecutive batches, enhancing cache hit rates and reducing redundant data transfers [24][25] Group 5: Results and Impact - CLM enhances the training capacity of 3DGS models by up to 6.1 times compared to pure GPU training baselines, enabling the training of larger models that improve scene reconstruction accuracy while lowering communication and unloading overhead [27]