Pith. sign in

REVIEW 3 cited by

VR-Pipe: Streamlining Hardware Graphics Pipeline for Volume Rendering

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2502.17078 v1 pith:XS673XBH submitted 2025-02-24 cs.GR cs.ARcs.CV

classification cs.GRcs.ARcs.CV
keywords graphicshardwarerenderingperformancepipelineradiancefieldvr-pipe
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original 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 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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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

    cs.AR 2025-07 conditional novelty 7.0 of 10

    GCC is a 3DGS accelerator with a Gaussian-wise, cross-stage conditional dataflow, achieving 5.24x area-normalized speedup and 3.35x energy efficiency over GSCore.

  2. DeGS: A Scalable 3DGS Architecture via Decoupled Workload Parsing and Reorganization

    cs.AR 2026-08 conditional novelty 6.0 of 10

    DeGS restructures 3DGS rendering into span parsing, task reorganization, and dense blending stages, achieving 1.8x-7.2x speedup and >80% scaling utilization over prior 3DGS accelerators.

  3. Lumina: Real-Time Mobile Neural Rendering by Exploiting Computational Redundancy

    cs.AR 2025-06 conditional novelty 6.0 of 10

    A mobile 3DGS rendering system that shares sorting across frames, caches pixel colors by significant Gaussian IDs, and adds a custom neural rendering unit to reach 4.5x speedup.

Pith tools