mlsanityData to 7 Oct 2026

Radiant Foam Rendering on a Graph Processor

arXiv 2601.04382 ↗First posted 7 Jan 2026cs.GR cs.CVcited by 1

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Abstract (arXiv)

Many emerging many-core accelerators replace a single large device memory with hundreds to thousands of lightweight cores, each owning only a small local SRAM and exchanging data via explicit on-chip communication. This organization offers high aggregate bandwidth, but it breaks a key assumption behind many volumetric rendering techniques: that rays can randomly access a large, unified scene representation. Rendering efficiently on such hardware therefore requires distributing both data and computation, keeping ray traversal mostly local, and structuring communication into predictable routes. We present a fully in-SRAM, distributed renderer for the Radiant Foam Voronoi-cell volumetric representation on the Graphcore Mk2 IPU(Intelligence Processing Unit), a many-core accelerator with tile-local SRAM and explicit inter-tile communication. Our system shards the scene across tiles and forwards rays between shards through a hierarchical routing overlay, enabling ray marching entirely from on-chip SRAM with predictable communication. On Mip-NeRF~360 scenes, the system attains near-interactive throughput of approximately 1 fps at 640x480 with image and depth map quality close to the original GPU-based Radiant Foam implementation, while keeping all scene data and ray state in on-chip SRAM. Beyond demonstrating feasibility, we analyze routing, memory, and scheduling bottlenecks that inform how future distributed-memory accelerators can better support irregular, data-movement-heavy rendering workloads.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 25 of its 53 references that are in the map. First posted on arXiv 7 Jan 2026.

Builds on

25 of 53 references.

References · 25

  1. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
  2. 2021Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fieldscited by 2,013
  3. 2020Neural Sparse Voxel Fieldscited by 959
  4. 2022Block-NeRF: Scalable Large Scene Neural View Synthesiscited by 720
  5. 2023Gaussian Splatting SLAMcited by 410
  6. 2021Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughscited by 396
  7. 2023SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAMcited by 386
  8. 2024A Survey on 3D Gaussian Splattingcited by 221
  9. 2024VastGaussian: Vast 3D Gaussians for Large Scene Reconstructioncited by 209
  10. 2023Tetra-NeRF: Representing Neural Radiance Fields Using Tetrahedracited by 57
  11. 2025Triangle Splatting for Real-Time Radiance Field Renderingcited by 50
  12. 2019Dissecting the Graphcore IPU Architecture via Microbenchmarkingcited by 47
13 more
  1. 2022RT-NeRF: Real-Time On-Device Neural Radiance Fields Towards Immersive AR/VR Renderingcited by 35
  2. 2024Sparse Voxels Rasterization: Real-time High-fidelity Radiance Field Renderingcited by 35
  3. 2025LinPrim: Linear Primitives for Differentiable Volumetric Renderingcited by 21
  4. 2025A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generationcited by 19
  5. 2020Bundle Adjustment on a Graph Processorcited by 15
  6. 2025GCC: A 3DGS Inference Architecture with Gaussian-Wise and Cross-Stage Conditional Processingcited by 10
  7. 2024Tetrahedron Splatting for 3D Generationcited by 9
  8. 2020Graphcore C2 Card performance for image-based deep learning application: A Reportcited by 5
  9. 2025Triangle Splatting+: Differentiable Rendering with Opaque Trianglescited by 5
  10. 2023Harnessing Manycore Processors with Distributed Memory for Accelerated Training of Sparse and Recurrent Modelscited by 4
  11. 2023Towards Neural Path Tracing in SRAMcited by 2
  12. 2025FlexNeRFer: A Multi-Dataflow, Adaptive Sparsity-Aware Accelerator for On-Device NeRF Renderingcited by 2
  13. 2025Efficient 3D Gaussian Splatting with Axis-Shared Rasterization and Order-independent Transmittancecited by 2

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