# Splatonic: Architecture Support for 3D Gaussian Splatting SLAM via Sparse Processing

arXiv: https://arxiv.org/abs/2511.18755 · HTML: https://mlsanity.com/paper/2511.18755/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2025.

## Built on by (most cited first, 4 of 4)

- [Towards Next-Generation SLAM: A Survey on 3DGS-SLAM Focusing on Performance, Robustness, and Future Directions](https://mlsanity.com/paper/2602.04251/) (2026)
- [DeGS: A Scalable 3DGS Architecture via Decoupled Workload Parsing and Reorganization](https://mlsanity.com/paper/2608.02099/) (2026)
- [Deltoris: Enabling Real-time VLA Inference in Embodied AI via Bit-level Sparsity and Speculative Inference](https://mlsanity.com/paper/2608.04428/) (2026)
- [From Splats to Silicon: Rethinking Computational Efficiency of 3DGS](https://mlsanity.com/paper/2609.06157/) (2026)

## Abstract (arXiv)

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× 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× speedup on the bottleneck stages and 14.6× 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× speedup and 4738.5× energy savings over mobile GPUs and up to 25.2× speedup and 241.1× energy savings over state-of-the-art accelerators, all with comparable accuracy.