# GCC: A 3DGS Inference Architecture with Gaussian-Wise and Cross-Stage Conditional Processing

arXiv: https://arxiv.org/abs/2507.15300 · HTML: https://mlsanity.com/paper/2507.15300/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2025.

## Built on by (most cited first, 10 of 10)

- [π-StepNFT: Wider Space Needs Finer Steps in Online RL for Flow-based VLAs](https://mlsanity.com/paper/2603.02083/) (2026)
- [Neo: Real-Time On-Device 3D Gaussian Splatting with Reuse-and-Update Sorting Acceleration](https://mlsanity.com/paper/2511.12930/) (2025)
- [Radiant Foam Rendering on a Graph Processor](https://mlsanity.com/paper/2601.04382/) (2026)
- [GRTX: Efficient Ray Tracing for 3D Gaussian-Based Rendering](https://mlsanity.com/paper/2601.20429/) (2026)
- [Local-GS: Accelerating 3D Gaussian Splatting via Tile-Local Warp Coherence](https://mlsanity.com/paper/2606.16566/) (2026)
- [DeGS: A Scalable 3DGS Architecture via Decoupled Workload Parsing and Reorganization](https://mlsanity.com/paper/2608.02099/) (2026)
- [From ex(p) to poly: Gaussian Splatting with Polynomial Kernels](https://mlsanity.com/paper/2603.18707/) (2026)
- [Rendering 3D Gaussians on a Graph Processor](https://mlsanity.com/paper/2607.15951/) (2026)
- [RoofGS: Roofline-Guided End-to-End Acceleration of 3D Gaussian Splatting](https://mlsanity.com/paper/2608.15785/) (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 leading neural rendering technique for high-fidelity view synthesis, prompting the development of dedicated 3DGS accelerators for resource-constrained platforms. The conventional decoupled preprocessing-rendering dataflow in existing accelerators has two major limitations: 1) a significant portion of preprocessed Gaussians are not used in rendering, and 2) the same Gaussian gets repeatedly loaded across different tile renderings, resulting in substantial computational and data movement overhead. To address these issues, we propose GCC, a novel accelerator designed for fast and energy-efficient 3DGS inference. GCC introduces a novel dataflow featuring: 1) textitcross-stage conditional processing, which interleaves preprocessing and rendering to dynamically skip unnecessary Gaussian preprocessing; and 2) textitGaussian-wise rendering, ensuring that all rendering operations for a given Gaussian are completed before moving to the next, thereby eliminating duplicated Gaussian loading. We also propose an alpha-based boundary identification method to derive compact and accurate Gaussian regions, thereby reducing rendering costs. We implement our GCC accelerator in 28nm technology. Extensive experiments demonstrate that GCC significantly outperforms the state-of-the-art 3DGS inference accelerator, GSCore, in both performance and energy efficiency.