mlsanityData to 7 Oct 2026

PRoGS: Progressive Rendering of Gaussian Splats

arXiv 2409.01761 ↗First posted 3 Sep 2024cs.CV cs.MMcited by 12

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Abstract (arXiv)

Over the past year, 3D Gaussian Splatting (3DGS) has received significant attention for its ability to represent 3D scenes in a perceptually accurate manner. However, it can require a substantial amount of storage since each splat's individual data must be stored. While compression techniques offer a potential solution by reducing the memory footprint, they still necessitate retrieving the entire scene before any part of it can be rendered. In this work, we introduce a novel approach for progressively rendering such scenes, aiming to display visible content that closely approximates the final scene as early as possible without loading the entire scene into memory. This approach benefits both on-device rendering applications limited by memory constraints and streaming applications where minimal bandwidth usage is preferred. To achieve this, we approximate the contribution of each Gaussian to the final scene and construct an order of prioritization on their inclusion in the rendering process. Additionally, we demonstrate that our approach can be combined with existing compression methods to progressively render (and stream) 3DGS scenes, optimizing bandwidth usage by focusing on the most important splats within a scene. Overall, our work establishes a foundation for making remotely hosted 3DGS content more quickly accessible to end-users in over-the-top consumption scenarios, with our results showing significant improvements in quality across all metrics compared to existing methods.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 13 of its 35 references that are in the map. First posted on arXiv 3 Sep 2024.

Builds on

13 of 35 references.

References · 13

  1. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
  2. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
  3. 2024SAM 2: Segment Anything in Images and Videoscited by 2,784
  4. 2021Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fieldscited by 2,013
  5. 2023LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPScited by 420
  6. 2023Compressed 3D Gaussian Splatting for Accelerated Novel View Synthesiscited by 232
  7. 2021BungeeNeRF: Progressive Neural Radiance Field for Extreme Multi-scale Scene Renderingcited by 228
  8. 2021Editing Conditional Radiance Fieldscited by 222
  9. 2023EAGLES: Efficient Accelerated 3D Gaussians with Lightweight EncodingScited by 191
  10. 2024Reducing the Memory Footprint of 3D Gaussian Splattingcited by 129
  11. 2023NeRFMeshing: Distilling Neural Radiance Fields into Geometrically-Accurate 3D Meshescited by 52
  12. 2022Streamable Neural Fieldscited by 13
1 more
  1. 2023Neural Progressive Meshescited by 12

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 7.

12 papers.