mlsanityData to 14 Oct 2026

DirectL: Efficient Radiance Fields Rendering for 3D Light Field Displays

arXiv 2407.14053 ↗First posted 19 Jul 2024cs.GR cs.CVcited by 1

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Abstract (arXiv)

Autostereoscopic display, despite decades of development, has not achieved extensive application, primarily due to the daunting challenge of 3D content creation for non-specialists. The emergence of Radiance Field as an innovative 3D representation has markedly revolutionized the domains of 3D reconstruction and generation. This technology greatly simplifies 3D content creation for common users, broadening the applicability of Light Field Displays (LFDs). However, the combination of these two fields remains largely unexplored. The standard paradigm to create optimal content for parallax-based light field displays demands rendering at least 45 slightly shifted views preferably at high resolution per frame, a substantial hurdle for real-time rendering. We introduce DirectL, a novel rendering paradigm for Radiance Fields on 3D displays. We thoroughly analyze the interweaved mapping of spatial rays to screen subpixels, precisely determine the light rays entering the human eye, and propose subpixel repurposing to significantly reduce the pixel count required for rendering. Tailored for the two predominant radiance fields--Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (3DGS), we propose corresponding optimized rendering pipelines that directly render the light field images instead of multi-view images. Extensive experiments across various displays and user study demonstrate that DirectL accelerates rendering by up to 40 times compared to the standard paradigm without sacrificing visual quality. Its rendering process-only modification allows seamless integration into subsequent radiance field tasks. Finally, we integrate DirectL into diverse applications, showcasing the stunning visual experiences and the synergy between LFDs and Radiance Fields, which unveils tremendous potential for commercialization applications. hrefdirect-l.github.ioProject Homepage

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 28 references that are in the map. First posted on arXiv 19 Jul 2024.

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7 of 28 references.

References · 7

  1. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
  2. 2021Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesiscited by 460
  3. 2023BakedSDF: Meshing Neural SDFs for Real-Time View Synthesiscited by 221
  4. 2023MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenescited by 218
  5. 2024A Hierarchical 3D Gaussian Representation for Real-Time Rendering of Very Large Datasetscited by 216
  6. 2016Learning-Based View Synthesis for Light Field Camerascited by 145
  7. 2023SMERF: Streamable Memory Efficient Radiance Fields for Real-Time Large-Scene Explorationcited by 62

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Citing papers per year: 2026: 1.

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