VR-Pipe: Streamlining Hardware Graphics Pipeline for Volume Rendering
Junseo Lee, Jaisung Kim, Junyong Park, Jaewoong Sim

TL;DR
VR-Pipe enhances hardware graphics pipelines for volume rendering, specifically radiance fields, by integrating native support for early termination and multi-granular tile binning, resulting in significant speedups with minimal hardware overhead.
Contribution
It introduces two hardware innovations—early termination support and tile binning—that optimize volume rendering performance in modern GPUs.
Findings
Achieves up to 2.78x speedup in rendering performance.
Seamlessly integrates into existing graphics hardware with negligible overhead.
Effectively supports state-of-the-art radiance field methods like 3D Gaussian splatting.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComputer Graphics and Visualization Techniques · 3D Shape Modeling and Analysis
MethodsSoftmax · Attention Is All You Need · SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
