Mixed Materials
Splatonic: Architecture Support for 3D Gaussian Splatting SLAM via Sparse Processing
| Τίτλος: | Splatonic: Architecture Support for 3D Gaussian Splatting SLAM via Sparse Processing |
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| Additional Titles: | 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. |
| Συγγραφείς: | Huang, Xiaotong, Zhu, He, Ma, Tianrui, Xiong, Yuxiang, Liu, Fangxin, He, Zhezhi, Gan, Yiming, Liu, Zihan, Leng, Jingwen, Feng, Yu, Guo, Minyi |
| Στοιχεία εκδότη: | 2025-11-24 2025-11-27 |
| Τύπος εγγράφου: | Mixed materials |
| Όροι ευρετηρίου: | Hardware Architecture, text |
| Σύνδεσμος: | |
| Διαθεσιμότητα: | Open access content. Open access content |
| Other Numbers: | COO oai:arXiv.org:2511.18755 1577658495 |
| Πηγή συνεισφοράς: | CORNELL UNIV From OAIster®, provided by the OCLC Cooperative. |
| Αριθμός Καταχώρησης: | edsoai.on1577658495 |
| Βάση Δεδομένων: | OAIster |
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