mlsanityData to 7 Oct 2026

SparseOIT: Improving Order-Independent Transparency 3DGS via Active Set Method

arXiv 2605.13855 ↗First posted 7 Apr 2026cs.GR cs.AI cs.CVcited by 0

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Abstract (arXiv)

3D Gaussian Splatting (3DGS) has received tremendous popularity over the past few years due to its photorealistic visual appearance. However, 3DGS uses volumetric rendering that is not suitable for objects with non-lambertian or transparent materials. To remedy this issue, a family of Order-Independent Transparency (OIT) rendering methods propose to remove or modify the depth sorting step in the 3DGS rendering equation. However, the potential of OIT-based method is still underexplored. In this paper, we observe that the OIT modifications to the rendering equation significantly reduce the inter-independence among individual gaussian splats, resulting in very sparse variable dependencies that can be harnessed by specific optimization techniques such as active set method. To this end, we propose SparseOIT, an OIT-based 3DGS reconstruction algorithm that maintains an active set of gaussian splats and enjoys an acceleration ratio that is proportional to the potential sparsity. SparseOIT is designed by jointly considering the OIT rendering equation, the reconstruction algorithm and the geometric regularization. Through extensive experiments, we demonstrate that SparseOIT outperforms existing methods in the OIT-family by a large margin and also achieves comparable performance to the state-of-the-art 3DGS reconstruction methods based on volumetric rendering. Project page:

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 21 of its 33 references that are in the map. First posted on arXiv 7 Apr 2026.

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21 of 33 references.

References · 21

  1. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  2. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
  3. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
  4. 2021Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fieldscited by 2,013
  5. 2021Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fieldscited by 1,957
  6. 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,607
  7. 2021Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstructioncited by 961
  8. 2023Compact 3D Gaussian Representation for Radiance Fieldcited by 365
  9. 2024Taming 3DGS: High-Quality Radiance Fields with Limited Resourcescited by 198
  10. 2023EAGLES: Efficient Accelerated 3D Gaussians with Lightweight EncodingScited by 191
  11. 2024StopThePop: Sorted Gaussian Splatting for View-Consistent Real-time Renderingcited by 107
  12. 2024FreGS: 3D Gaussian Splatting with Progressive Frequency Regularizationcited by 103
9 more
  1. 2024Speedy-Splat: Fast 3D Gaussian Splatting with Sparse Pixels and Sparse Primitivescited by 90
  2. 20243DGS-LM: Faster Gaussian-Splatting Optimization with Levenberg-Marquardtcited by 42
  3. 2025DashGaussian: Optimizing 3D Gaussian Splatting in 200 Secondscited by 37
  4. 2024AdR-Gaussian: Accelerating Gaussian Splatting with Adaptive Radiuscited by 34
  5. 2024Efficient Perspective-Correct 3D Gaussian Splatting Using Hybrid Transparencycited by 33
  6. 2024Sort-free Gaussian Splatting via Weighted Sum Renderingcited by 27
  7. 20253DGS2: Near Second-order Converging 3D Gaussian Splattingcited by 19
  8. 2025StochasticSplats: Stochastic Rasterization for Sorting-Free 3D Gaussian Splattingcited by 17
  9. 2024Balanced 3DGS: Gaussian-wise Parallelism Rendering with Fine-Grained Tilingcited by 12

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