{"ID":2837324,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.18755","arxiv_id":"2511.18755","title":"Splatonic: Architecture Support for 3D Gaussian Splatting SLAM via Sparse Processing","abstract":"3D Gaussian splatting (3DGS) has emerged as a promising direction for SLAM due to its high-fidelity reconstruction and rapid convergence. However, 3DGS-SLAM algorithms remain impractical for mobile platforms due to their high computational cost, especially for their tracking process. This work introduces Splatonic, a sparse and efficient real-time 3DGS-SLAM algorithm-hardware co-design for resource-constrained devices. Inspired by classical SLAMs, we propose an adaptive sparse pixel sampling algorithm that reduces the number of rendered pixels by up to 256$\\times$ while retaining accuracy. To unlock this performance potential on mobile GPUs, we design a novel pixel-based rendering pipeline that improves hardware utilization via Gaussian-parallel rendering and preemptive $α$-checking. Together, these optimizations yield up to 121.7$\\times$ speedup on the bottleneck stages and 14.6$\\times$ end-to-end speedup on off-the-shelf GPUs. To further address new bottlenecks introduced by our rendering pipeline, we propose a pipelined architecture that simplifies the overall design while addressing newly emerged bottlenecks in projection and aggregation. Evaluated across four 3DGS-SLAM algorithms, Splatonic achieves up to 274.9$\\times$ speedup and 4738.5$\\times$ energy savings over mobile GPUs and up to 25.2$\\times$ speedup and 241.1$\\times$ energy savings over state-of-the-art accelerators, all with comparable accuracy.","short_abstract":"3D Gaussian splatting (3DGS) has emerged as a promising direction for SLAM due to its high-fidelity reconstruction and rapid convergence. However, 3DGS-SLAM algorithms remain impractical for mobile platforms due to their high computational cost, especially for their tracking process. This work introduces Splatonic, a s...","url_abs":"https://arxiv.org/abs/2511.18755","url_pdf":"https://arxiv.org/pdf/2511.18755v2","authors":"[\"Xiaotong Huang\",\"He Zhu\",\"Tianrui Ma\",\"Yuxiang Xiong\",\"Fangxin Liu\",\"Zhezhi He\",\"Yiming Gan\",\"Zihan Liu\",\"Jingwen Leng\",\"Yu Feng\",\"Minyi Guo\"]","published":"2025-11-24T04:27:45Z","proceeding":"cs.AR","tasks":"[\"cs.AR\"]","methods":"[]","has_code":false}