mlsanityData to 7 Oct 2026

TWINGS: Thin Plate Splines Warp-aligned Initialization for Sparse-View Gaussian Splatting

arXiv 2605.22069 ↗First posted 21 May 2026cs.CV cs.LGcited by 0

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Abstract (arXiv)

Novel view synthesis from sparse-view inputs poses a significant challenge in 3D computer vision, particularly for achieving high-quality scene reconstructions with limited viewpoints. We introduce TWINGS, a framework that enhances 3D Gaussian Splatting (3DGS) by directly addressing point sparsity. We employ Thin Plate Splines (TPS), a smooth non-rigid deformation model that minimizes bending energy to estimate a globally coherent warp from control-point correspondences, to align backprojected points from estimated depth with triangulated 3D control points, yielding calibrated backprojected points. By sampling these calibrated points near the control points, TWINGS provides a fast and geometrically accurate initialization for 3DGS, ultimately improving structural detail preservation and color fidelity in reconstructed scenes. Extensive experiments on DTU, LLFF, and Mip-NeRF360 demonstrate that TWINGS consistently outperforms existing methods, delivering detailed and accurate reconstructions under sparse-view scenarios.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 34 of its 47 references that are in the map. First posted on arXiv 21 May 2026.

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34 of 47 references.

References · 34

  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
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  11. 2021MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereocited by 735
  12. 2021RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputscited by 614
22 more
  1. 2021Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesiscited by 464
  2. 2024Depth Pro: Sharp Monocular Metric Depth in Less Than a Secondcited by 395
  3. 2023FreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularizationcited by 310
  4. 2016LIFT: Learned Invariant Feature Transformcited by 305
  5. 2023FSGS: Real-Time Few-shot View Synthesis using Gaussian Splattingcited by 278
  6. 2024DNGaussian: Optimizing Sparse-View 3D Gaussian Radiance Fields with Global-Local Depth Normalizationcited by 256
  7. 2023SparseNeRF: Distilling Depth Ranking for Few-shot Novel View Synthesiscited by 247
  8. 2021Function4D: Real-time Human Volumetric Capture from Very Sparse Consumer RGBD Sensorscited by 246
  9. 2023Depth-Regularized Optimization for 3D Gaussian Splatting in Few-Shot Imagescited by 172
  10. 2019GLU-Net: Global-Local Universal Network for Dense Flow and Correspondencescited by 139
  11. 2024DN-Splatter: Depth and Normal Priors for Gaussian Splatting and Meshingcited by 136
  12. 2019Neural-Guided RANSAC: Learning Where to Sample Model Hypothesescited by 129
  13. 2022QuadTree Attention for Vision Transformerscited by 129
  14. 2022MatchFormer: Interleaving Attention in Transformers for Feature Matchingcited by 126
  15. 2024CoR-GS: Sparse-View 3D Gaussian Splatting via Co-Regularizationcited by 120
  16. 2018DGC-Net: Dense Geometric Correspondence Networkcited by 67
  17. 2025DropGaussian: Structural Regularization for Sparse-view Gaussian Splattingcited by 65
  18. 2024Binocular-Guided 3D Gaussian Splatting with View Consistency for Sparse View Synthesiscited by 41
  19. 2024Generalizable Human Gaussians for Sparse View Synthesiscited by 40
  20. 2025CoMapGS: Covisibility Map-based Gaussian Splatting for Sparse Novel View Synthesiscited by 10
  21. 2024SPARS3R: Semantic Prior Alignment and Regularization for Sparse 3D Reconstructioncited by 8
  22. 2024Dense Point Clouds Matter: Dust-GS for Scene Reconstruction from Sparse Viewpointscited by 6

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