Local-GS: An Order-Independent Gaussian Splatting Training Accelerator Exploiting Splat Locality

3D Gaussian Splatting has emerged as the SOTA approach for 3D representation and view synthesis. While Gaussian Splatting has demonstrated impressive capability and rendering quality on desktop GPUs, achieving on-demand training on resource-constrained edge devices is still challenging. In this work, we identified the training bottleneck from a few perspectives including algorithm splat locality and the limited memory and hardware under-utilization. To address these problems, we present Local-GS, a 3D Gaussian Splatting training accelerator with order-independent rendering to break the depth-wise data dependency between overlapping Gaussians. We further incorporate a parallel pixel intersection test unit to schedule thread workload based on Gaussian splat locality and improve hardware utilization. A set of unified training-rendering cores are designed to achieve efficient splat-level parallel rendering and gradient propagation. Our Local-GS is implemented in 7 nm and is evaluated by several real-world 3D scenes. Compared to edge Jetson NX GPU, Local-GS achieve 26.9-53 $\times$ training speedup and three orders of magnitude efficiency boost.