mlsanityData to 13 Oct 2026

LinPrim: Linear Primitives for Differentiable Volumetric Rendering

arXiv 2501.16312 ↗First posted 27 Jan 2025cs.CVcited by 21

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Abstract (arXiv)

Volumetric rendering has become central to modern novel view synthesis methods, which use differentiable rendering to optimize 3D scene representations directly from observed views. While many recent works build on NeRF or 3D Gaussians, we explore an alternative volumetric scene representation. More specifically, we introduce two new scene representations based on linear primitives - octahedra and tetrahedra - both of which define homogeneous volumes bounded by triangular faces. To optimize these primitives, we present a differentiable rasterizer that runs efficiently on GPUs, allowing end-to-end gradient-based optimization while maintaining real-time rendering capabilities. Through experiments on real-world datasets, we demonstrate comparable performance to state-of-the-art volumetric methods while requiring fewer primitives to achieve similar reconstruction fidelity. Our findings deepen the understanding of 3D representations by providing insights into the fidelity and performance characteristics of transparent polyhedra and suggest that adopting novel primitives can expand the available design space.

Key facts. Cited by 21 papers in the map (data through 13 Oct 2026). Builds on 26 of its 35 references that are in the map. First posted on arXiv 27 Jan 2025.

Builds on

26 of 35 references.

References · 26

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,093
  2. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,170
  3. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,712
  4. 2021Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fieldscited by 2,021
  5. 2021Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fieldscited by 1,958
  6. 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,706
  7. 2020D-NeRF: Neural Radiance Fields for Dynamic Scenescited by 1,462
  8. 20242D Gaussian Splatting for Geometrically Accurate Radiance Fieldscited by 1,070
  9. 2023DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creationcited by 839
  10. 2023ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenescited by 752
  11. 2019Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervisioncited by 699
  12. 2023Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fieldscited by 669
14 more
  1. 2023Mip-Splatting: Alias-free 3D Gaussian Splattingcited by 663
  2. 2022Point-NeRF: Point-based Neural Radiance Fieldscited by 503
  3. 2023GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splattingcited by 333
  4. 2022MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architecturescited by 311
  5. 2022NeRF-Editing: Geometry Editing of Neural Radiance Fieldscited by 216
  6. 20243D Gaussian Splatting as Markov Chain Monte Carlocited by 214
  7. 2024Mini-Splatting: Representing Scenes with a Constrained Number of Gaussianscited by 210
  8. 2021Point-Based Neural Rendering with Per-View Optimizationcited by 190
  9. 2023Delicate Textured Mesh Recovery from NeRF via Adaptive Surface Refinementcited by 106
  10. 20243D Convex Splatting: Radiance Field Rendering with 3D Smooth Convexescited by 50
  11. 2024EVER: Exact Volumetric Ellipsoid Rendering for Real-time View Synthesiscited by 47
  12. 2024Beyond Gaussians: Fast and High-Fidelity 3D Splatting with Linear Kernelscited by 22
  13. 2024Efficient Scene Modeling via Structure-Aware and Region-Prioritized 3D Gaussianscited by 14
  14. 2024Mesh2NeRF: Direct Mesh Supervision for Neural Radiance Field Representation and Generationcited by 5

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 11; 2026: 10.

21 papers.

Sort:

  1. 2025Triangle Splatting for Real-Time Radiance Field Renderingcited by 50
  2. 2026Faster-GS: Analyzing and Improving Gaussian Splatting Optimizationcited by 13
  3. 2025MeshSplats: Mesh-Based Rendering with Gaussian Splatting Initializationcited by 9
  4. 2025Splat the Net: Radiance Fields with Splattable Neural Primitivescited by 7
  5. 2025Radiance Meshes for Volumetric Reconstructioncited by 7
  6. 2025Triangle Splatting+: Differentiable Rendering with Opaque Trianglescited by 5
  7. 2025ConeGS: Error-Guided Densification Using Pixel Cones for Improved Reconstruction With Fewer Primitivescited by 5
  8. 2025ContraGS: Codebook-Condensed and Trainable Gaussian Splatting for Fast, Memory-Efficient Reconstructioncited by 4
  9. 2025Radiant Triangle Soup with Soft Connectivity Forces for 3D Reconstruction and Novel View Synthesiscited by 2
  10. 2025SVR-GS: Spatially Variant Regularization for Probabilistic Masks in 3D Gaussian Splattingcited by 2
  11. 2025REdiSplats: Ray Tracing for Editable Gaussian Splattingcited by 1
  12. 2025A Bag of Tricks for Efficient Implicit Neural Point Cloudscited by 1
9 more
  1. 2026Radiant Foam Rendering on a Graph Processorcited by 1
  2. 2026Confidence-Based Mesh Extraction from 3D Gaussianscited by 1
  3. 2026DVSM: Decoder-only View Synthesis Model Done Rightcited by 1
  4. 2026DiffBMP: Differentiable Rendering with Bitmap Primitivescited by 0
  5. 2026MLP Splatting: Object-Centric Neural Fieldscited by 0
  6. 2026XPR: An Extensible Cross-Platform Point-Based Differentiable Renderercited by 0
  7. 2026Deformable Triangle Splatting: Flexible Primitives for Real-Time Radiance Field Renderingcited by 0
  8. 2026Differentiable Voronoi Ray Tracing Beyond Rasterization Speedscited by 0
  9. 2026Compact Neural Appearance Models for Efficient Gaussian Splattingcited by 0