Gaussian Blending Unit: An Edge GPU Plug-in for Real-Time Gaussian-Based Rendering in AR/VR
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Abstract (arXiv)
The rapidly advancing field of Augmented and Virtual Reality (AR/VR) demands real-time, photorealistic rendering on resource-constrained platforms. 3D Gaussian Splatting, delivering state-of-the-art (SOTA) performance in rendering efficiency and quality, has emerged as a promising solution across a broad spectrum of AR/VR applications. However, despite its effectiveness on high-end GPUs, it struggles on edge systems like the Jetson Orin NX Edge GPU, achieving only 7-17 FPS -- well below the over 60 FPS standard required for truly immersive AR/VR experiences. Addressing this challenge, we perform a comprehensive analysis of Gaussian-based AR/VR applications and identify the Gaussian Blending Stage, which intensively calculates each Gaussian's contribution at every pixel, as the primary bottleneck. In response, we propose a Gaussian Blending Unit (GBU), an edge GPU plug-in module for real-time rendering in AR/VR applications. Notably, our GBU can be seamlessly integrated into conventional edge GPUs and collaboratively supports a wide range of AR/VR applications. Specifically, GBU incorporates an intra-row sequential shading (IRSS) dataflow that shades each row of pixels sequentially from left to right, utilizing a two-step coordinate transformation. When directly deployed on a GPU, the proposed dataflow achieved a non-trivial 1.72x speedup on real-world static scenes, though still falls short of real-time rendering performance. Recognizing the limited compute utilization in the GPU-based implementation, GBU enhances rendering speed with a dedicated rendering engine that balances the workload across rows by aggregating computations from multiple Gaussians. Experiments across representative AR/VR applications demonstrate that our GBU provides a unified solution for on-device real-time rendering while maintaining SOTA rendering quality.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Mar 2025.
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Citing papers per year: 2025: 4; 2026: 4.
8 papers.
- 2025GS-Scale: Unlocking Large-Scale 3D Gaussian Splatting Training via Host Offloadingcited by 5
- 2025Neo: Real-Time On-Device 3D Gaussian Splatting with Reuse-and-Update Sorting Accelerationcited by 5
- 2025Geometry-Consistent 4D Gaussian Splatting for Sparse-Input Dynamic View Synthesiscited by 3
- 2025Efficient 3D Gaussian Splatting with Axis-Shared Rasterization and Order-independent Transmittancecited by 2
- 2026GRTX: Efficient Ray Tracing for 3D Gaussian-Based Renderingcited by 1
- 2026Splaxel: Efficient Distributed Training of 3D Gaussian Splatting for Large-scale Scene Reconstruction via Pixel-level Communicationcited by 1
- 2026DeGS: A Scalable 3DGS Architecture via Decoupled Workload Parsing and Reorganizationcited by 1
- 2026From Splats to Silicon: Rethinking Computational Efficiency of 3DGScited by 0
- 2026From Splats to Silicon: Rethinking Computational Efficiency of 3DGS5 Sep 2026
- 2026DeGS: A Scalable 3DGS Architecture via Decoupled Workload Parsing and Reorganization3 Aug 2026
- 2026Splaxel: Efficient Distributed Training of 3D Gaussian Splatting for Large-scale Scene Reconstruction via Pixel-level Communication17 Jun 2026
- 2026GRTX: Efficient Ray Tracing for 3D Gaussian-Based Rendering28 Jan 2026
- 2025Geometry-Consistent 4D Gaussian Splatting for Sparse-Input Dynamic View Synthesis28 Nov 2025
- 2025Neo: Real-Time On-Device 3D Gaussian Splatting with Reuse-and-Update Sorting Acceleration17 Nov 2025
- 2025GS-Scale: Unlocking Large-Scale 3D Gaussian Splatting Training via Host Offloading19 Sep 2025
- 2025Efficient 3D Gaussian Splatting with Axis-Shared Rasterization and Order-independent Transmittance8 Jun 2025