mlsanityData to 14 Oct 2026

VR-Pipe: Streamlining Hardware Graphics Pipeline for Volume Rendering

arXiv 2502.17078 ↗First posted 24 Feb 2025cs.GR cs.AR cs.CVcited by 3

Not on the map. Why?

Abstract (arXiv)

Graphics rendering that builds on machine learning and radiance fields is gaining significant attention due to its outstanding quality and speed in generating photorealistic images from novel viewpoints. However, prior work has primarily focused on evaluating its performance through software-based rendering on programmable shader cores, leaving its performance when exploiting fixed-function graphics units largely unexplored. In this paper, we investigate the performance implications of performing radiance field rendering on the hardware graphics pipeline. In doing so, we implement the state-of-the-art radiance field method, 3D Gaussian splatting, using graphics APIs and evaluate it across synthetic and real-world scenes on today's graphics hardware. Based on our analysis, we present VR-Pipe, which seamlessly integrates two innovations into graphics hardware to streamline the hardware pipeline for volume rendering, such as radiance field methods. First, we introduce native hardware support for early termination by repurposing existing special-purpose hardware in modern GPUs. Second, we propose multi-granular tile binning with quad merging, which opportunistically blends fragments in shader cores before passing them to fixed-function blending units. Our evaluation shows that VR-Pipe greatly improves rendering performance, achieving up to a 2.78x speedup over the conventional graphics pipeline with negligible hardware overhead.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 16 of its 46 references that are in the map. First posted on arXiv 24 Feb 2025.

Builds on

16 of 46 references.

References · 16

  1. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
  2. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,723
  3. 2021Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fieldscited by 2,032
  4. 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,613
  5. 2021Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstructioncited by 964
  6. 2020Neural Sparse Voxel Fieldscited by 959
  7. 2021Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughscited by 398
  8. 2023Compact 3D Gaussian Representation for Radiance Fieldcited by 372
  9. 2022MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architecturescited by 311
  10. 2023Compressed 3D Gaussian Splatting for Accelerated Novel View Synthesiscited by 239
  11. 2024CityGaussian: Real-time High-quality Large-Scale Scene Rendering with Gaussianscited by 192
  12. 2024StopThePop: Sorted Gaussian Splatting for View-Consistent Real-time Renderingcited by 111
4 more
  1. 2024GES: Generalized Exponential Splatting for Efficient Radiance Field Renderingcited by 92
  2. 2023Instant-3D: Instant Neural Radiance Field Training Towards On-Device AR/VR 3D Reconstructioncited by 51
  3. 2024Cicero: Addressing Algorithmic and Architectural Bottlenecks in Neural Rendering by Radiance Warping and Memory Optimizationscited by 11
  4. 2023Hardware Acceleration of Neural Graphicscited by 10

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.