DeGS: A Scalable 3DGS Architecture via Decoupled Workload Parsing and Reorganization
3D Gaussian Splatting (3DGS) has emerged as a leading technique for real-time novel view synthesis, yet existing 3DGS accelerators suffer from poor architectural scalability: increasing the number of PEs leads to marginal performance improvement during rendering. We identify that the root cause is the tightly coupled ``checking-while-blending'' dataflow, which exacerbates PE underutilization caused by spatial redundancy from irregular Gaussian coverage and temporal redundancy from asynchronous pixel-wise termination under parallel execution. To address this issue, we propose DeGS, a scalable architecture for efficient 3DGS inference. To systematically eliminate the redundancies inherent i...
| Authors | Minnan Pei, Gang Li, Zeyu Zhu et al. |
|---|---|
| Primary category | cs.AR |
| Published | 3 August 2026 |
| Last updated at source | 3 August 2026 |
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