SpeedyGS compresses 3D visual data 160x with near-zero decoding latency
Researchers from Nanjing University and Huawei have introduced SpeedyGS, a content-aware compression method for 3D Gaussian Splatting that features two-stage optimization. The technology achieves a 160x reduction in model size while significantly accelerating decoding latency and optimization speed on consumer-grade hardware.
Key Takeaways
- Reduces 3DGS model storage by up to 160x compared to vanilla datasets which often exceed 400MB.
- Accelerates the training-as-compressing optimization process by 9x over existing state-of-the-art methods.
- Introduces fixed-length coding variants that reduce decoding overhead to nearly zero for real-time applications.
- Utilizes a two-stage approach that decouples adaptive quantization/pruning from local autoregressive statistical coding.
Why It Matters
The high storage and bandwidth requirements of 3DGS have been the primary bottlenecks for mobile and interactive deployments. SpeedyGS provides a viable path for high-fidelity 3D assets to be delivered over standard consumer networks and rendered with immediate responsiveness. By streamlining the decoding process—previously taking 10+ seconds for advanced codecs—this development lowers the barrier for volumetric video and immersive telepresence. Strategically, it signals a shift from purely maximizing compression ratios toward optimizing the full end-to-end pipeline of delivery and playback. Watch for the integration of these features into mobile SoC rendering engines to support real-time 3D consumer applications.
Additional Context
The development of SpeedyGS follows a period of intense competition to standardize 3D Gaussian Splatting (3DGS) evaluation. Per Arxiv reporting in December 2025, tools like Splatwizard were introduced to unify benchmarks for pruning and entropy coding, specifically targeting complex datasets like Mip-NeRF360. This trend reflects the industry's need for consistent metrics as researchers move beyond novel view synthesis into practical streaming and storage applications.
Simultaneous research has expanded the 3DGS footprint into autonomous driving and simulation. Per NVIDIA in June 2026, 3DGS is increasingly used for physically accurate AI environments within the NVIDIA Cosmos foundation models. These applications require high-fidelity rendering of sensor data, such as the Lidar-ready 'SplatAD' method presented at CVPR 2025, which increases rendering speed by an order of magnitude over previous NeRF-based sensors.
Recent commercial interest has stabilized around mobile and XR systems. Per EurekAlert in March 2026, ShanghaiTech University researchers released an end-to-end processing platform designed to support multi-platform rendering across Unity, iOS, and Web environments. This ecosystem growth highlights the importance of the Nanjing-Huawei work, as faster decoding directly impacts the commercial viability of 3D content on battery-constrained handheld and head-mounted devices.
Read full article at arxiv.org
Enjoy our coverage?
Add StreamingMeme as a preferred source on Google to see more of our streaming news at the top of your Search results.
Add as preferred source